'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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
   _UpperCAmelCase						:    Optional[int]     					=					'''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : str=None      ,		__snake_case       : int=None							):
     if attention_mask is None:
          _A								=     np.where(input_ids != config.pad_token_id      ,		1      ,		0							)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }







@require_flax
class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Union[str, Any],   UpperCamelCase__			: int,   UpperCamelCase__			: Tuple=13,   UpperCamelCase__			: int=7,   UpperCamelCase__			: Tuple=True,   UpperCamelCase__			: Union[str, Any]=False,   UpperCamelCase__			: int=99,   UpperCamelCase__			: Optional[Any]=16,   UpperCamelCase__			: Dict=2,   UpperCamelCase__			: Union[str, Any]=4,   UpperCamelCase__			: Optional[int]=4,   UpperCamelCase__			: Dict="gelu",   UpperCamelCase__			: List[str]=0.1,   UpperCamelCase__			: Optional[Any]=0.1,   UpperCamelCase__			: Any=20,   UpperCamelCase__			: List[Any]=2,   UpperCamelCase__			: Optional[int]=1,   UpperCamelCase__			: Union[str, Any]=0,   UpperCamelCase__			: Tuple=16,   UpperCamelCase__			: Optional[Any]=16,   UpperCamelCase__			: Optional[int]=0.02,   )     -> int:
        _A								=     parent
        _A								=     batch_size
        _A								=     seq_length
        _A								=     is_training
        _A								=     use_labels
        _A								=     vocab_size
        _A								=     hidden_size
        _A								=     num_hidden_layers
        _A								=     num_attention_heads
        _A								=     intermediate_size
        _A								=     hidden_act
        _A								=     hidden_dropout_prob
        _A								=     attention_probs_dropout_prob
        _A								=     max_position_embeddings
        _A								=     eos_token_id
        _A								=     pad_token_id
        _A								=     bos_token_id
        _A								=     embed_dim
        _A								=     word_embed_proj_dim
        _A								=     initializer_range
        _A								=     False
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> str:
        _A								=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1],   self.vocab_size    ),   3,   self.vocab_size    )
        _A								=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),   dtype=np.intaa    )),   -1    )

        _A								=     OPTConfig(
            vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   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,   embed_dim=self.embed_dim,   is_encoder_decoder=UpperCamelCase__,   word_embed_proj_dim=self.word_embed_proj_dim,   initializer_range=self.initializer_range,   use_cache=UpperCamelCase__,   )
        _A								=     prepare_opt_inputs_dict(UpperCamelCase__,   UpperCamelCase__    )
        return config, inputs_dict
   def   __UpperCAmelCase      (			self			: Any    )     -> Optional[Any]:
        _A		,					_A								=     self.prepare_config_and_inputs()
        return config, inputs_dict
   def   __UpperCAmelCase      (			self			: Optional[int],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Tuple,   UpperCamelCase__			: int    )     -> int:
        _A								=     20
        _A								=     model_class_name(UpperCamelCase__    )

        _A								=     inputs_dict['input_ids']
        _A								=     inputs_dict['attention_mask']

        _A								=     model.init_cache(input_ids.shape[0],   UpperCamelCase__    )
        _A								=     jnp.ones((input_ids.shape[0], max_length),   dtype='i4'    )

        _A								=     jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1    )[None, :],   (input_ids.shape[0], input_ids.shape[-1] - 1),   )
        _A								=     model(
            input_ids[:, :-1],   attention_mask=UpperCamelCase__,   past_key_values=UpperCamelCase__,   position_ids=UpperCamelCase__,   )

        _A								=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],   dtype='i4'    )
        _A								=     model(
            input_ids[:, -1:],   attention_mask=UpperCamelCase__,   past_key_values=outputs_cache.past_key_values,   position_ids=UpperCamelCase__,   )

        _A								=     model(UpperCamelCase__    )

        _A								=     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   __UpperCAmelCase      (			self			: Optional[Any],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: List[str],   UpperCamelCase__			: Tuple    )     -> int:
        _A								=     20
        _A								=     model_class_name(UpperCamelCase__    )

        _A		,					_A								=     (
            inputs_dict['input_ids'],
            inputs_dict['attention_mask'],
        )

        _A								=     jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])    ),
            ],   axis=-1,   )

        _A								=     model.init_cache(input_ids.shape[0],   UpperCamelCase__    )
        _A								=     jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1    )[None, :],   (input_ids.shape[0], input_ids.shape[-1] - 1),   )

        _A								=     model(
            input_ids[:, :-1],   attention_mask=UpperCamelCase__,   past_key_values=UpperCamelCase__,   position_ids=UpperCamelCase__,   )
        _A								=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],   dtype='i4'    )
        _A								=     model(
            input_ids[:, -1:],   past_key_values=outputs_cache.past_key_values,   attention_mask=UpperCamelCase__,   position_ids=UpperCamelCase__,   )

        _A								=     model(UpperCamelCase__,   attention_mask=UpperCamelCase__    )

        _A								=     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_					(		_UpperCamelCase						,						unittest.TestCase						,						_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   __lowerCAmelCase    						=      () if is_flax_available() else ()
   def   __UpperCAmelCase      (			self			: List[str]    )     -> List[Any]:
        _A								=     FlaxOPTModelTester(self    )
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> List[str]:
        _A		,					_A								=     self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Dict    )     -> List[Any]:
        _A		,					_A								=     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(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )

   @slow
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Union[str, Any]:
        for model_class_name in self.all_model_classes:
             _A								=     model_class_name.from_pretrained('facebook/opt-125m'    )
             _A								=     np.ones((1, 1)    ) * model.config.eos_token_id
             _A								=     model(UpperCamelCase__    )
             self.assertIsNotNone(UpperCamelCase__    )







@require_sentencepiece
@require_flax
class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   @slow
   def   __UpperCAmelCase      (			self			: int    )     -> Tuple:
        _A								=     FlaxOPTModel.from_pretrained('facebook/opt-350m'    )
        _A								=     jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]    )
        _A								=     model(input_ids=UpperCamelCase__    ).last_hidden_state
        _A								=     (1, 11, 5_12)
        self.assertEqual(output.shape,   UpperCamelCase__    )
        _A								=     jnp.array(
            [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]    )
        self.assertTrue(jnp.allclose(output[:, :3, :3],   UpperCamelCase__,   atol=4e-2    )    )







@require_flax
@slow
class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: List[str]    )     -> Tuple:
        super().setUp()
        _A								=     'facebook/opt-350m'

   def   __UpperCAmelCase      (			self			: Any    )     -> Any:
        _A								=     FlaxOPTForCausalLM.from_pretrained(self.path_model    )
        _A								=     GPTaTokenizer.from_pretrained(self.path_model    )

        _A								=     [
            'Today is a beautiful day and I want to',
            'In the city of',
            'Paris is the capital of France and',
            'Computers and mobile phones have taken',
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        _A								=     tokenizer(UpperCamelCase__,   return_tensors='jax',   padding=UpperCamelCase__,   add_special_tokens=UpperCamelCase__    )
        _A								=     model(inputs.input_ids,   attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
        _A								=     jnp.array(
            [
                [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
                [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
            ]    )
        self.assertTrue(jnp.allclose(UpperCamelCase__,   UpperCamelCase__,   atol=4e-2    )    )

        _A								=     jax.jit(UpperCamelCase__    )
        _A								=     model(inputs.input_ids,   attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
        self.assertTrue(jnp.allclose(UpperCamelCase__,   UpperCamelCase__,   atol=4e-2    )    )







@require_flax
@slow
class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   @property
   def   __UpperCAmelCase      (			self			: str    )     -> str:
        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]
   def   __UpperCAmelCase      (			self			: List[str]    )     -> Tuple:
        _A								=     'facebook/opt-125m'

        _A								=     [
            'Today is a beautiful day and I want to',
            'In the city of New York, the city',
            'Paris is the capital of France and the capital',
            'Computers and mobile phones have taken over the',
        ]

        _A								=     []

        _A								=     FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )
        _A								=     GPTaTokenizer.from_pretrained(UpperCamelCase__    )

        for prompt in self.prompts:
             _A								=     tokenizer(UpperCamelCase__,   return_tensors='jax'    ).input_ids

             _A								=     model.generate(UpperCamelCase__,   max_length=10    )
             _A								=     generated_ids[0]

             _A								=     tokenizer.batch_decode(UpperCamelCase__,   skip_special_tokens=UpperCamelCase__    )
             predicted_outputs += generated_string

        self.assertListEqual(UpperCamelCase__,   UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Any:
        _A								=     'facebook/opt-350m'

        _A								=     [
            'Today is a beautiful day and I want to',
            'In the city of San Francisco, the city',
            'Paris is the capital of France and the capital',
            'Computers and mobile phones have taken over the',
        ]

        _A								=     []
        _A								=     FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )
        _A								=     GPTaTokenizer.from_pretrained(UpperCamelCase__    )

        for prompt in self.prompts:
             _A								=     tokenizer(UpperCamelCase__,   return_tensors='jax'    ).input_ids

             _A								=     model.generate(UpperCamelCase__,   max_length=10    )
             _A								=     generated_ids[0]

             _A								=     tokenizer.batch_decode(UpperCamelCase__,   skip_special_tokens=UpperCamelCase__    )
             predicted_outputs += generated_string

        self.assertListEqual(UpperCamelCase__,   UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Any    )     -> Union[str, Any]:
        _A								=     'facebook/opt-125m'
        _A								=     [
            'Today is a beautiful day and I want to thank',
            'In the city of Rome Canaver Canaver Canaver Canaver',
        ]
        _A								=     FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )
        _A								=     GPTaTokenizer.from_pretrained(UpperCamelCase__    )
        _A								=     tokenizer(
            [
                'Today is a beautiful day and I want to',
                'In the city of',
            ],   return_tensors='jax',   padding=UpperCamelCase__,   )

        _A								=     jax.jit(model.generate    )

        _A								=     jit_generate(inputs['input_ids'],   attention_mask=inputs['attention_mask']    ).sequences

        _A								=     tokenizer.batch_decode(UpperCamelCase__,   skip_special_tokens=UpperCamelCase__    )

        self.assertIsNotNone(UpperCamelCase__,   UpperCamelCase__    )

   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> List[str]:
        _A								=     'facebook/opt-350m'

        _A								=     GPTaTokenizer.from_pretrained(UpperCamelCase__    )
        _A								=     FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )

        _A								=     'left'

        # use different length sentences to test batching
        _A								=     [
            'Hello, my dog is a little',
            'Today, I',
        ]

        _A								=     tokenizer(UpperCamelCase__,   return_tensors='jax',   padding=UpperCamelCase__    )
        _A								=     inputs['input_ids']

        _A								=     model.generate(input_ids=UpperCamelCase__,   attention_mask=inputs['attention_mask'],   trace=UpperCamelCase__    )

        _A								=     tokenizer(sentences[0],   return_tensors='jax'    ).input_ids
        _A								=     model.generate(input_ids=UpperCamelCase__    )

        _A								=     inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
        _A								=     tokenizer(sentences[1],   return_tensors='jax'    ).input_ids
        _A								=     model.generate(input_ids=UpperCamelCase__,   max_length=model.config.max_length - num_paddings    )

        _A								=     tokenizer.batch_decode(outputs[0],   skip_special_tokens=UpperCamelCase__    )
        _A								=     tokenizer.decode(output_non_padded[0][0],   skip_special_tokens=UpperCamelCase__    )
        _A								=     tokenizer.decode(output_padded[0][0],   skip_special_tokens=UpperCamelCase__    )

        _A								=     [
            'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
            'Today, I was in the middle of a conversation with a friend about the',
        ]
        self.assertListEqual(UpperCamelCase__,   UpperCamelCase__    )
        self.assertListEqual(UpperCamelCase__,   [non_padded_sentence, padded_sentence]    )




'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: NestedDataStructureLike[PathLike],   UpperCamelCase__			: Optional[NamedSplit] = None,   UpperCamelCase__			: Optional[Features] = None,   UpperCamelCase__			: str = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Optional[Any],   )     -> Tuple:
        super().__init__(
            UpperCamelCase__,   split=UpperCamelCase__,   features=UpperCamelCase__,   cache_dir=UpperCamelCase__,   keep_in_memory=UpperCamelCase__,   streaming=UpperCamelCase__,   num_proc=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     path_or_paths if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else {self.split: path_or_paths}
        _A								=     Csv(
            cache_dir=UpperCamelCase__,   data_files=UpperCamelCase__,   features=UpperCamelCase__,   **UpperCamelCase__,   )

   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        # Build iterable dataset
        if self.streaming:
             _A								=     self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
             _A								=     None
             _A								=     None
             _A								=     None
             _A								=     None

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase__,   download_mode=UpperCamelCase__,   verification_mode=UpperCamelCase__,   base_path=UpperCamelCase__,   num_proc=self.num_proc,   )
             _A								=     self.builder.as_dataset(
                 split=self.split,   verification_mode=UpperCamelCase__,   in_memory=self.keep_in_memory    )
        return dataset







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dataset,   UpperCamelCase__			: Union[PathLike, BinaryIO],   UpperCamelCase__			: Optional[int] = None,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Dict,   )     -> Any:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

        _A								=     dataset
        _A								=     path_or_buf
        _A								=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A								=     num_proc
        _A								=     'utf-8'
        _A								=     to_csv_kwargs
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     self.to_csv_kwargs.pop('path_or_buf',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('header',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('index',   UpperCamelCase__    )

        if isinstance(self.path_or_buf,   (str, bytes, os.PathLike)    ):
             with open(self.path_or_buf,   'wb+'    ) as buffer:
                  _A								=     self._write(file_obj=UpperCamelCase__,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        else:
             _A								=     self._write(file_obj=self.path_or_buf,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        return written
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Optional[int]    )     -> str:
        _A		,					_A		,					_A		,					_A								=     args

        _A								=     query_table(
            table=self.dataset.data,   key=slice(UpperCamelCase__,   offset + self.batch_size    ),   indices=self.dataset._indices,   )
        _A								=     batch.to_pandas().to_csv(
            path_or_buf=UpperCamelCase__,   header=header if (offset == 0) else False,   index=UpperCamelCase__,   **UpperCamelCase__    )
        return csv_str.encode(self.encoding    )

   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: BinaryIO,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Dict,   **UpperCamelCase__			: Optional[int]    )     -> int:
        _A								=     0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0,   len(self.dataset    ),   self.batch_size    ),   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                  _A								=     self._batch_csv((offset, header, index, to_csv_kwargs)    )
                  written += file_obj.write(UpperCamelCase__    )

        else:
             _A		,					_A								=     len(self.dataset    ), self.batch_size
             with multiprocessing.Pool(self.num_proc    ) as pool:
                  for csv_str in logging.tqdm(
                      pool.imap(
                          self._batch_csv,   [(offset, header, index, to_csv_kwargs) for offset in range(0,   UpperCamelCase__,   UpperCamelCase__    )],   ),   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                       written += file_obj.write(UpperCamelCase__    )

        return written



