




'''simple docstring'''






import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


A_ 			=		{}
for task, _ in pipeline_test_mapping.items():
      A_ 			=		{"pt": None, "tf": None}


# DO **NOT** add item to this set (unless the reason is approved)
A_ 			=		{
    "tests/models/esm/test_modeling_esmfold.py",  # The pipeline test mapping is added to `test_modeling_esm.py`
}






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[Any]:

  if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "pt"
  elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "tf"
  elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
    return "flax"
  else:
    return None






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
  # Use the cached results
  if PIPELINE_TEST_MAPPING[task].get(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		) is not None:
    return PIPELINE_TEST_MAPPING[task][framework]

  snake_case__					:      str  			=						pipeline_test_mapping[task]['test']
  snake_case__					:      int  			=						None

  if framework == "pt":
    snake_case__					:      Dict  			=						getattr(__SCREAMING_SNAKE_CASE							,			'model_mapping'							,			__SCREAMING_SNAKE_CASE		)
  elif framework == "tf":
    snake_case__					:      List[Any]  			=						getattr(__SCREAMING_SNAKE_CASE							,			'tf_model_mapping'							,			__SCREAMING_SNAKE_CASE		)

  if mapping is not None:
    snake_case__					:      List[str]  			=						dict(mapping.items()		)

  # cache the results
  snake_case__					:      Optional[int]  			=						mapping
  return mapping






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      str:
  snake_case__					:      Any  			=						get_framework(__SCREAMING_SNAKE_CASE		)
  if framework is None:
    return None
  snake_case__					:      str  			=						get_mapping_for_task(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
  if mapping is None:
    return None

  snake_case__					:      Union[str, Any]  			=						list({model_class.config_class for model_class in test_class.all_model_classes}		)
  if len(__SCREAMING_SNAKE_CASE		) != 1:
    raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'		)

  # This could be a list/tuple of model classes, but it's rare.
  snake_case__					:      Optional[int]  			=						mapping.get(config_classes[0]							,			__SCREAMING_SNAKE_CASE		)
  if isinstance(__SCREAMING_SNAKE_CASE							,			(tuple, list)		):
    snake_case__					:      str  			=						sorted(__SCREAMING_SNAKE_CASE							,			key=lambda __SCREAMING_SNAKE_CASE		: x.__name__		)

  return model_class






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      int:
  snake_case__					:      Tuple  			=						[(task, get_model_for_pipeline_test(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)) for task in pipeline_test_mapping]
  snake_case__					:      Tuple  			=						sorted([(task, model) for task, model in mapping if model is not None]							,			key=lambda __SCREAMING_SNAKE_CASE		: x[0]		)

  return dict(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
  snake_case__					:      Any  			=						get_framework(__SCREAMING_SNAKE_CASE		)
  if framework == "pt":
    snake_case__					:      Tuple  			=						'torch'
  snake_case__					:      Union[str, Any]  			=						'{}'

  snake_case__					:      int  			=						get_pipeline_model_mapping(__SCREAMING_SNAKE_CASE		)
  if len(__SCREAMING_SNAKE_CASE		) == 0:
    return ""

  snake_case__					:      Optional[Any]  			=						[]
  for task, model_classes in mapping.items():
    if isinstance(__SCREAMING_SNAKE_CASE							,			(tuple, list)		):
      # A list/tuple of model classes
      snake_case__					:      Any  			=						'(' + ', '.join([x.__name__ for x in model_classes]		) + ')'
    else:
      # A single model class
      snake_case__					:      str  			=						model_classes.__name__
    texts.append(f"\"{task}\": {value}"		)
  snake_case__					:      Dict  			=						'{' + ', '.join(__SCREAMING_SNAKE_CASE		) + '}'
  snake_case__					:      Any  			=						f"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"

  return text






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      int:
  snake_case__					:      Tuple  			=						{'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
  if not issubclass(__SCREAMING_SNAKE_CASE							,			unittest.TestCase		):
    return False
  return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]		)		) > 0






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[Any]:
  snake_case__					:      List[Any]  			=						[x for x in get_test_classes(__SCREAMING_SNAKE_CASE		) if is_valid_test_class(__SCREAMING_SNAKE_CASE		)]

  snake_case__					:      int  			=						None
  for test_class in test_classes:
    # If a test class has defined `pipeline_model_mapping`, let's take it
    if getattr(__SCREAMING_SNAKE_CASE							,			'pipeline_model_mapping'							,			__SCREAMING_SNAKE_CASE		) is not None:
      snake_case__					:      Any  			=						test_class
      break
    # Take the test class with the shortest name (just a heuristic)
  if target_test_class is None and len(__SCREAMING_SNAKE_CASE		) > 0:
    snake_case__					:      Optional[int]  			=						sorted(__SCREAMING_SNAKE_CASE							,			key=lambda __SCREAMING_SNAKE_CASE		: (len(x.__name__		), x.__name__)		)[0]

  return target_test_class






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
  snake_case__					:      int  			=						start_idx
  for idx, line in enumerate(lines[start_idx:]		):
    snake_case__					:      str  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
    if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
      snake_case__					:      Tuple  			=						start_idx + idx
    elif idx > 0 and indent <= indent_level:
      # Outside the definition block of `pipeline_model_mapping`
      break

  return end_idx






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=False		)						->      Optional[int]:
  if getattr(__SCREAMING_SNAKE_CASE							,			'pipeline_model_mapping'							,			__SCREAMING_SNAKE_CASE		) is not None:
    if not overwrite:
      return "", -1

  snake_case__					:      Optional[Any]  			=						get_pipeline_model_mapping_string(__SCREAMING_SNAKE_CASE		)
  if len(__SCREAMING_SNAKE_CASE		) == 0:
    return "", -1
  snake_case__					:      str  			=						line_to_add + '\n'

  # The code defined the class `test_class`
  snake_case__					:      Tuple  			=						inspect.getsourcelines(__SCREAMING_SNAKE_CASE		)
  # `inspect` gives the code for an object, including decorator(s) if any.
  # We (only) need the exact line of the class definition.
  for idx, line in enumerate(__SCREAMING_SNAKE_CASE		):
    if line.lstrip().startswith('class '		):
      snake_case__					:      int  			=						class_lines[idx:]
      class_start_line_no += idx
      break
  snake_case__					:      List[Any]  			=						class_start_line_no + len(__SCREAMING_SNAKE_CASE		) - 1

  # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
  # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
  # block that appears in a `test_class`.
  snake_case__					:      Optional[int]  			=						None
  # The indent level of the line at `class_lines[start_idx]` (if defined)
  snake_case__					:      Tuple  			=						0
  # To record if `pipeline_model_mapping` is found in `test_class`.
  snake_case__					:      str  			=						None
  for idx, line in enumerate(__SCREAMING_SNAKE_CASE		):
    if line.strip().startswith('all_model_classes = '		):
      snake_case__					:      str  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
      snake_case__					:      int  			=						idx
    elif line.strip().startswith('all_generative_model_classes = '		):
      snake_case__					:      Union[str, Any]  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
      snake_case__					:      Dict  			=						idx
    elif line.strip().startswith('pipeline_model_mapping = '		):
      snake_case__					:      Dict  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
      snake_case__					:      int  			=						idx
      snake_case__					:      List[str]  			=						line
      break

  if start_idx is None:
    return "", -1
  # Find the ending index (inclusive) of the above found block.
  snake_case__					:      Union[str, Any]  			=						find_block_ending(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

  # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
  # `is_timm_available()` instead of `is_torch_available()`.
  # Keep leading and trailing whitespaces
  snake_case__					:      Union[str, Any]  			=						re.compile(r'\s(is_\S+?_available\(\))\s'		)
  for line in class_lines[start_idx : end_idx + 1]:
    snake_case__					:      Optional[Any]  			=						r.search(__SCREAMING_SNAKE_CASE		)
    if backend_condition is not None:
      # replace the leading and trailing whitespaces to the space character " ".
      snake_case__					:      Optional[int]  			=						' ' + backend_condition[0][1:-1] + ' '
      snake_case__					:      List[str]  			=						r.sub(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      break

  if def_line is None:
    # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
    # `all_model_classes` or `all_generative_model_classes`.
    snake_case__					:      int  			=						end_idx
  else:
    # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
    snake_case__					:      List[Any]  			=						start_idx - 1
    # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
    for idx in range(__SCREAMING_SNAKE_CASE							,			end_idx + 1		):
      # These lines are going to be removed before writing to the test file.
      snake_case__					:      Optional[int]  			=						None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
  snake_case__					:      List[Any]  			=						[x.__name__ for x in test_class.__bases__]
  if "PipelineTesterMixin" not in parent_classes:
    # Put `PipelineTesterMixin` just before `unittest.TestCase`
    snake_case__					:      Dict  			=						[x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
    if "TestCase" in parent_classes:
      # Here we **assume** the original string is always with `unittest.TestCase`.
      _parent_classes.append('unittest.TestCase'		)
    snake_case__					:      Optional[int]  			=						', '.join(_parent_classes		)
    for idx, line in enumerate(__SCREAMING_SNAKE_CASE		):
      # Find the ending of the declaration of `test_class`
      if line.strip().endswith('):'		):
        # mark the lines of the declaration of `test_class` to be removed
        for _idx in range(idx + 1		):
          snake_case__					:      Any  			=						None  # noqa
        break
        # Add the new, one-line, class declaration for `test_class`
    snake_case__					:      int  			=						f"class {test_class.__name__}({parent_classes}):\n"

  # Add indentation
  snake_case__					:      Optional[int]  			=						' ' * indent_level + line_to_add
  # Insert `pipeline_model_mapping` to `class_lines`.
  # (The line at `target_idx` should be kept by definition!)
  snake_case__					:      Any  			=						class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
  # Remove the lines that are marked to be removed
  snake_case__					:      Optional[Any]  			=						[x for x in class_lines if x is not None]

  # Move from test class to module (in order to write to the test file)
  snake_case__					:      Union[str, Any]  			=						inspect.getsourcelines(inspect.getmodule(__SCREAMING_SNAKE_CASE		)		)[0]
  # Be careful with the 1-off between line numbers and array indices
  snake_case__					:      int  			=						module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
  snake_case__					:      int  			=						''.join(__SCREAMING_SNAKE_CASE		)

  snake_case__					:      Optional[Any]  			=						inspect.getsourcefile(__SCREAMING_SNAKE_CASE		)
  with open(__SCREAMING_SNAKE_CASE							,			'w'							,			encoding='UTF-8'							,			newline='\n'		) as fp:
    fp.write(__SCREAMING_SNAKE_CASE		)

  return line_to_add






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=False		)						->      Dict:
  snake_case__					:      Optional[int]  			=						find_test_class(__SCREAMING_SNAKE_CASE		)
  if test_class:
    add_pipeline_model_mapping(__SCREAMING_SNAKE_CASE							,			overwrite=__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      A_ 			=		argparse.ArgumentParser()
      parser.add_argument(
          "--test_file", type=str, help="A path to the test file, starting with the repository's `tests` directory."
      )
      parser.add_argument(
          "--all",
          action="store_true",
          help="If to check and modify all test files.",
      )
      parser.add_argument(
          "--overwrite",
          action="store_true",
          help="If to overwrite a test class if it has already defined `pipeline_model_mapping`.",
      )
      A_ 			=		parser.parse_args()

      if not args.all and not args.test_file:
            raise ValueError("Please specify either `test_file` or pass `--all` to check/modify all test files.")
      elif args.all and args.test_file:
            raise ValueError("Only one of `--test_file` and `--all` could be specified.")

      A_ 			=		[]
      if args.test_file:
            A_ 			=		[args.test_file]
      else:
            A_ 			=		os.path.join("tests", "models", "**", "test_modeling_*.py")
            for test_file in glob.glob(pattern):
                  # `Flax` is not concerned at this moment
                  if not test_file.startswith("test_modeling_flax_"):
                        test_files.append(test_file)

      for test_file in test_files:
            if test_file in TEST_FILE_TO_IGNORE:
                  print(F'[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.')
                  continue
            add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)






'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A_ 			=		"platform"

      import jax
      import jax.numpy as jnp

      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			)						->      Union[str, Any]:
      if attention_mask is None:
            snake_case__					:      Union[str, Any]  			=						np.where(input_ids != config.pad_token_id							,			1							,			0		)
      if decoder_attention_mask is None:
            snake_case__					:      int  			=						np.where(decoder_input_ids != config.pad_token_id							,			1							,			0		)
      if head_mask is None:
            snake_case__					:      Optional[int]  			=						np.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
            snake_case__					:      str  			=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
            snake_case__					:      int  			=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }






class 					lowercase_			:





     def __init__(    self    :				Any    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Optional[Any]=13    ,       __lowerCamelCase    :				str=7    ,       __lowerCamelCase    :				Optional[int]=True    ,       __lowerCamelCase    :				List[str]=False    ,       __lowerCamelCase    :				str=99    ,       __lowerCamelCase    :				List[str]=16    ,       __lowerCamelCase    :				Optional[Any]=2    ,       __lowerCamelCase    :				Tuple=4    ,       __lowerCamelCase    :				Any=4    ,       __lowerCamelCase    :				Optional[int]="gelu"    ,       __lowerCamelCase    :				Optional[Any]=0.1    ,       __lowerCamelCase    :				List[Any]=0.1    ,       __lowerCamelCase    :				Optional[int]=32    ,       __lowerCamelCase    :				Optional[Any]=2    ,       __lowerCamelCase    :				Union[str, Any]=1    ,       __lowerCamelCase    :				Dict=0    ,       __lowerCamelCase    :				Tuple=0.0_2    ,       ):
           snake_case__					:      Tuple  			=						parent
           snake_case__					:      Any  			=						batch_size
           snake_case__					:      Union[str, Any]  			=						seq_length
           snake_case__					:      Optional[Any]  			=						is_training
           snake_case__					:      Union[str, Any]  			=						use_labels
           snake_case__					:      Union[str, Any]  			=						vocab_size
           snake_case__					:      Any  			=						hidden_size
           snake_case__					:      Tuple  			=						num_hidden_layers
           snake_case__					:      Tuple  			=						num_attention_heads
           snake_case__					:      str  			=						intermediate_size
           snake_case__					:      Union[str, Any]  			=						hidden_act
           snake_case__					:      Optional[Any]  			=						hidden_dropout_prob
           snake_case__					:      str  			=						attention_probs_dropout_prob
           snake_case__					:      Optional[int]  			=						max_position_embeddings
           snake_case__					:      Union[str, Any]  			=						eos_token_id
           snake_case__					:      str  			=						pad_token_id
           snake_case__					:      Tuple  			=						bos_token_id
           snake_case__					:      str  			=						initializer_range





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      List[Any]  			=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size )    ,       3    ,       self.vocab_size )
           snake_case__					:      Tuple  			=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,       dtype=np.intaa ))    ,       -1 )

           snake_case__					:      Optional[int]  			=						shift_tokens_right(__lowerCamelCase    ,       1    ,       2 )

           snake_case__					:      Union[str, Any]  			=						MarianConfig(
               vocab_size=self.vocab_size    ,       d_model=self.hidden_size    ,       encoder_layers=self.num_hidden_layers    ,       decoder_layers=self.num_hidden_layers    ,       encoder_attention_heads=self.num_attention_heads    ,       decoder_attention_heads=self.num_attention_heads    ,       encoder_ffn_dim=self.intermediate_size    ,       decoder_ffn_dim=self.intermediate_size    ,       dropout=self.hidden_dropout_prob    ,       attention_dropout=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       eos_token_id=self.eos_token_id    ,       bos_token_id=self.bos_token_id    ,       pad_token_id=self.pad_token_id    ,       initializer_range=self.initializer_range    ,       use_cache=__lowerCamelCase    ,       )
           snake_case__					:      Union[str, Any]  			=						prepare_marian_inputs_dict(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__			,       snake_case__					:      Optional[int]  			=						self.prepare_config_and_inputs()
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Dict ):
           snake_case__					:      Optional[int]  			=						20
           snake_case__					:      str  			=						model_class_name(__lowerCamelCase )

           snake_case__					:      Dict  			=						model.encode(inputs_dict['input_ids'] )

           snake_case__			,       snake_case__					:      int  			=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           snake_case__					:      Dict  			=						model.init_cache(decoder_input_ids.shape[0]    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      Optional[int]  			=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,       dtype='i4' )

           snake_case__					:      List[str]  			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )
           snake_case__					:      Dict  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      List[str]  			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      Tuple  			=						model.decode(
               decoder_input_ids[:, -1:]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      Union[str, Any]  			=						model.decode(__lowerCamelCase    ,       __lowerCamelCase )

           snake_case__					:      Tuple  			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
           self.parent.assertTrue(diff < 1E-3    ,       msg=F"Max diff is {diff}" )





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Optional[int] ):
           snake_case__					:      int  			=						20
           snake_case__					:      Tuple  			=						model_class_name(__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						model.encode(inputs_dict['input_ids'] )

           snake_case__			,       snake_case__					:      Dict  			=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           snake_case__					:      List[str]  			=						jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
               ]    ,       axis=-1    ,       )

           snake_case__					:      Union[str, Any]  			=						model.init_cache(decoder_input_ids.shape[0]    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      List[str]  			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )

           snake_case__					:      List[Any]  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )
           snake_case__					:      Tuple  			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      int  			=						model.decode(
               decoder_input_ids[:, -1:]    ,       __lowerCamelCase    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_attention_mask=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      Dict  			=						model.decode(__lowerCamelCase    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
           self.parent.assertTrue(diff < 1E-3    ,       msg=F"Max diff is {diff}" )






@require_flax
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase	,					lowerCAmelCase_       ):
     A_       							= True
     A_       							= (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     A_       							= (FlaxMarianMTModel,) if is_flax_available() else ()





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Any  			=						FlaxMarianModelTester(self )





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__			,       snake_case__					:      str  			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      Union[str, Any]  			=						self._prepare_for_class(__lowerCamelCase    ,       __lowerCamelCase )
                       snake_case__					:      List[str]  			=						model_class(__lowerCamelCase )

                       @jax.jit
                       def encode_jitted(__lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Optional[Any]=None    ,       **__lowerCamelCase    :				List[str] ):
                             return model.encode(input_ids=__lowerCamelCase    ,       attention_mask=__lowerCamelCase )

                       with self.subTest('JIT Enabled' ):
                             snake_case__					:      List[Any]  			=						encode_jitted(**__lowerCamelCase ).to_tuple()

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      Dict  			=						encode_jitted(**__lowerCamelCase ).to_tuple()

                       self.assertEqual(len(__lowerCamelCase )    ,       len(__lowerCamelCase ) )
                       for jitted_output, output in zip(__lowerCamelCase    ,       __lowerCamelCase ):
                             self.assertEqual(jitted_output.shape    ,       output.shape )





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__			,       snake_case__					:      Dict  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      List[str]  			=						model_class(__lowerCamelCase )
                       snake_case__					:      Union[str, Any]  			=						model.encode(inputs_dict['input_ids']    ,       inputs_dict['attention_mask'] )

                       snake_case__					:      str  			=						{
                           'decoder_input_ids': inputs_dict['decoder_input_ids'],
                           'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                           'encoder_outputs': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(__lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Optional[Any] ):
                             return model.decode(
                                 decoder_input_ids=__lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       encoder_outputs=__lowerCamelCase    ,       )

                       with self.subTest('JIT Enabled' ):
                             snake_case__					:      List[str]  			=						decode_jitted(**__lowerCamelCase ).to_tuple()

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      str  			=						decode_jitted(**__lowerCamelCase ).to_tuple()

                       self.assertEqual(len(__lowerCamelCase )    ,       len(__lowerCamelCase ) )
                       for jitted_output, output in zip(__lowerCamelCase    ,       __lowerCamelCase ):
                             self.assertEqual(jitted_output.shape    ,       output.shape )





     @slow
     def  _lowerCAmelCase			(    self    :				List[str] ):
           for model_class_name in self.all_model_classes:
                 snake_case__					:      Any  			=						model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de' )
                 # FlaxMarianForSequenceClassification expects eos token in input_ids
                 snake_case__					:      List[Any]  			=						np.ones((1, 1) ) * model.config.eos_token_id
                 snake_case__					:      Union[str, Any]  			=						model(__lowerCamelCase )
                 self.assertIsNotNone(__lowerCamelCase )






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	unittest.TestCase       ):
     A_       							= None
     A_       							= None





     @classmethod
     def  _lowerCAmelCase			(    cls    :				Union[str, Any] ):
           snake_case__					:      List[Any]  			=						F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
           return cls





     @cached_property
     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           return MarianTokenizer.from_pretrained(self.model_name )





     @property
     def  _lowerCAmelCase			(    self    :				int ):
           return self.tokenizer.eos_token_id





     @cached_property
     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      FlaxMarianMTModel  			=						FlaxMarianMTModel.from_pretrained(self.model_name )
           self.assertEqual(model.config.decoder_start_token_id    ,       model.config.pad_token_id )
           return model





     def  _lowerCAmelCase			(    self    :				Tuple    ,       **__lowerCamelCase    :				List[Any] ):
           snake_case__					:      Dict  			=						self.translate_src_text(**__lowerCamelCase )
           self.assertListEqual(self.expected_text    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[str]    ,       **__lowerCamelCase    :				Dict ):
           snake_case__					:      str  			=						self.tokenizer(self.src_text    ,       padding=__lowerCamelCase    ,       return_tensors='np'    ,       **__lowerCamelCase )
           snake_case__					:      Any  			=						self.model.generate(
               model_inputs.input_ids    ,       attention_mask=model_inputs.attention_mask    ,       num_beams=2    ,       max_length=128    ,       ).sequences
           snake_case__					:      List[str]  			=						self.tokenizer.batch_decode(__lowerCamelCase    ,       skip_special_tokens=__lowerCamelCase )
           return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "fr"
     A_       							= [
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
     ]
     A_       							= [
         "Je suis une petite grenouille.",
         "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "fr"
     A_       							= "en"
     A_       							= [
         "Donnez moi le micro.",
         "Tom et Mary étaient assis à une table.",  # Accents
     ]
     A_       							= [
         "Give me the microphone.",
         "Tom and Mary were sitting at a table.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "mt"
     A_       							= "en"
     A_       							= ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
     A_       							= ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]





     @slow
     def  _lowerCAmelCase			(    self    :				Dict ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "de"
     A_       							= [
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
         "Tom asked his teacher for advice.",
         "That's how I would do it.",
         "Tom really admired Mary's courage.",
         "Turn around and close your eyes.",
     ]
     A_       							= [
         "Ich bin ein kleiner Frosch.",
         "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
         "Tom bat seinen Lehrer um Rat.",
         "So würde ich das machen.",
         "Tom bewunderte Marias Mut wirklich.",
         "Drehen Sie sich um und schließen Sie die Augen.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				Dict ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "zh"
     A_       							= ["My name is Wolfgang and I live in Berlin"]
     A_       							= ["我叫沃尔夫冈 我住在柏林"]





     @slow
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "ru"
     A_       							= "fr"
     A_       							= ["Он показал мне рукопись своей новой пьесы."]
     A_       							= ["Il m'a montré le manuscrit de sa nouvelle pièce."]





     @slow
     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "ROMANCE"
     A_       							= [
         ">>fr<< Don't spend so much time watching TV.",
         ">>pt<< Your message has been sent.",
         ">>es<< He's two years older than me.",
     ]
     A_       							= [
         "Ne passez pas autant de temps à regarder la télé.",
         "A sua mensagem foi enviada.",
         "Es dos años más viejo que yo.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				int ):
           self._assert_generated_batch_equal_expected()
