



'''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											=			'''platform'''

					import jax
					import jax.numpy as jnp

					from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def   lowercase_	(   lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  Tuple=None			,							lowerCAmelCase__	:  List[str]=None ):

			"""simple docstring"""





			if attention_mask is None:
						__UpperCAmelCase   :      str										=     np.where(input_ids != config.pad_token_id			,							1			,							0 )
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}



@require_flax
class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=13 ,       __UpperCAmelCase=7 ,       __UpperCAmelCase=True ,       __UpperCAmelCase=False ,       __UpperCAmelCase=99 ,       __UpperCAmelCase=16 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=4 ,       __UpperCAmelCase=4 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=20 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=1 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase=16 ,       __UpperCAmelCase=16 ,       __UpperCAmelCase=0.02 ,       )       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     parent
					__UpperCAmelCase   :      int										=     batch_size
					__UpperCAmelCase   :      List[str]										=     seq_length
					__UpperCAmelCase   :      List[str]										=     is_training
					__UpperCAmelCase   :      Tuple										=     use_labels
					__UpperCAmelCase   :      str										=     vocab_size
					__UpperCAmelCase   :      Dict										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      List[Any]										=     num_attention_heads
					__UpperCAmelCase   :      Tuple										=     intermediate_size
					__UpperCAmelCase   :      List[str]										=     hidden_act
					__UpperCAmelCase   :      List[str]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Any										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      Tuple										=     max_position_embeddings
					__UpperCAmelCase   :      Any										=     eos_token_id
					__UpperCAmelCase   :      List[Any]										=     pad_token_id
					__UpperCAmelCase   :      int										=     bos_token_id
					__UpperCAmelCase   :      Dict										=     embed_dim
					__UpperCAmelCase   :      Dict										=     word_embed_proj_dim
					__UpperCAmelCase   :      Any										=     initializer_range
					__UpperCAmelCase   :      Union[str, Any]										=     False





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size				) ,       3 ,       self.vocab_size				)
					__UpperCAmelCase   :      Optional[int]										=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,       dtype=np.intaa				)) ,       -1				)

					__UpperCAmelCase   :      List[Any]										=     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 ,       )
					__UpperCAmelCase   :      Dict										=     prepare_opt_inputs_dict(__UpperCAmelCase ,       __UpperCAmelCase				)
					return config, inputs_dict





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Dict										=     self.prepare_config_and_inputs()
					return config, inputs_dict





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     20
					__UpperCAmelCase   :      Any										=     model_class_name(__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     inputs_dict["""input_ids"""]
					__UpperCAmelCase   :      str										=     inputs_dict["""attention_mask"""]

					__UpperCAmelCase   :      List[Any]										=     model.init_cache(input_ids.shape[0] ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     jnp.ones((input_ids.shape[0], max_length) ,       dtype="""i4"""				)

					__UpperCAmelCase   :      List[Any]										=     jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1) ,       )
					__UpperCAmelCase   :      str										=     model(
					    input_ids[:, :-1] ,       attention_mask=__UpperCAmelCase ,       past_key_values=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Optional[Any]										=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype="""i4"""				)
					__UpperCAmelCase   :      Union[str, Any]										=     model(
					    input_ids[:, -1:] ,       attention_mask=__UpperCAmelCase ,       past_key_values=outputs_cache.past_key_values ,       position_ids=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Tuple										=     model(__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[Any]										=     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 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     20
					__UpperCAmelCase   :      Any										=     model_class_name(__UpperCAmelCase				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      Optional[Any]										=     (
					    inputs_dict["""input_ids"""],
					    inputs_dict["""attention_mask"""],
					)

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

					__UpperCAmelCase   :      Tuple										=     model.init_cache(input_ids.shape[0] ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[int]										=     jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1) ,       )

					__UpperCAmelCase   :      Dict										=     model(
					    input_ids[:, :-1] ,       attention_mask=__UpperCAmelCase ,       past_key_values=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )
					__UpperCAmelCase   :      Optional[int]										=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype="""i4"""				)
					__UpperCAmelCase   :      Optional[Any]										=     model(
					    input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Optional[int]										=     model(__UpperCAmelCase ,       attention_mask=__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     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 _A  (    __SCREAMING_SNAKE_CASE	,				unittest.TestCase	,				__SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    str						      =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		_SCREAMING_SNAKE_CASE					:    List[str]						      =			() if is_flax_available() else ()





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     FlaxOPTModelTester(self				)





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      str										=     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 							__A       (     self				)       ->     int:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      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(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)





		@slow
		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					for model_class_name in self.all_model_classes:
								__UpperCAmelCase   :      Dict										=     model_class_name.from_pretrained("""facebook/opt-125m"""				)
								__UpperCAmelCase   :      List[Any]										=     np.ones((1, 1)				) * model.config.eos_token_id
								__UpperCAmelCase   :      Tuple										=     model(__UpperCAmelCase				)
								self.assertIsNotNone(__UpperCAmelCase				)



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





		@slow
		def 							__A       (     self				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     FlaxOPTModel.from_pretrained("""facebook/opt-350m"""				)
					__UpperCAmelCase   :      Union[str, Any]										=     jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]				)
					__UpperCAmelCase   :      Dict										=     model(input_ids=__UpperCAmelCase				).last_hidden_state
					__UpperCAmelCase   :      Dict										=     (1, 11, 512)
					self.assertEqual(output.shape ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     jnp.array(
					    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]				)
					self.assertTrue(jnp.allclose(output[:, :3, :3] ,       __UpperCAmelCase ,       atol=4E-2				)				)



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





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					super().setUp()
					__UpperCAmelCase   :      Dict										=     """facebook/opt-350m"""





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     FlaxOPTForCausalLM.from_pretrained(self.path_model				)
					__UpperCAmelCase   :      Tuple										=     GPTaTokenizer.from_pretrained(self.path_model				)

					__UpperCAmelCase   :      Dict										=     [
					    """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
					__UpperCAmelCase   :      List[Any]										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax""" ,       padding=__UpperCAmelCase ,       add_special_tokens=__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     model(inputs.input_ids ,       attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
					__UpperCAmelCase   :      List[Any]										=     jnp.array(
					    [
					        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
					        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
					        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
					        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
					    ]				)
					self.assertTrue(jnp.allclose(__UpperCAmelCase ,       __UpperCAmelCase ,       atol=4E-2				)				)

					__UpperCAmelCase   :      Optional[Any]										=     jax.jit(__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     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 _A  (    unittest.TestCase    ):





		@property
		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					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 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     """facebook/opt-125m"""

					__UpperCAmelCase   :      Union[str, Any]										=     [
					    """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""",
					]

					__UpperCAmelCase   :      Tuple										=     []

					__UpperCAmelCase   :      str										=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase				)
					__UpperCAmelCase   :      List[Any]										=     GPTaTokenizer.from_pretrained(__UpperCAmelCase				)

					for prompt in self.prompts:
								__UpperCAmelCase   :      Any										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax"""				).input_ids

								__UpperCAmelCase   :      Dict										=     model.generate(__UpperCAmelCase ,       max_length=10				)
								__UpperCAmelCase   :      Union[str, Any]										=     generated_ids[0]

								__UpperCAmelCase   :      str										=     tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)
								predicted_outputs += generated_string

					self.assertListEqual(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     """facebook/opt-350m"""

					__UpperCAmelCase   :      List[Any]										=     [
					    """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""",
					]

					__UpperCAmelCase   :      Tuple										=     []
					__UpperCAmelCase   :      Any										=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase				)
					__UpperCAmelCase   :      List[str]										=     GPTaTokenizer.from_pretrained(__UpperCAmelCase				)

					for prompt in self.prompts:
								__UpperCAmelCase   :      int										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax"""				).input_ids

								__UpperCAmelCase   :      List[str]										=     model.generate(__UpperCAmelCase ,       max_length=10				)
								__UpperCAmelCase   :      Optional[Any]										=     generated_ids[0]

								__UpperCAmelCase   :      Union[str, Any]										=     tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)
								predicted_outputs += generated_string

					self.assertListEqual(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     """facebook/opt-125m"""
					__UpperCAmelCase   :      Optional[Any]										=     [
					    """Today is a beautiful day and I want to thank""",
					    """In the city of Rome Canaver Canaver Canaver Canaver""",
					]
					__UpperCAmelCase   :      Tuple										=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     GPTaTokenizer.from_pretrained(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     tokenizer(
					    [
					        """Today is a beautiful day and I want to""",
					        """In the city of""",
					    ] ,       return_tensors="""jax""" ,       padding=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Any										=     jax.jit(model.generate				)

					__UpperCAmelCase   :      int										=     jit_generate(inputs["""input_ids"""] ,       attention_mask=inputs["""attention_mask"""]				).sequences

					__UpperCAmelCase   :      Union[str, Any]										=     tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)

					self.assertIsNotNone(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     """facebook/opt-350m"""

					__UpperCAmelCase   :      str										=     GPTaTokenizer.from_pretrained(__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[int]										=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     """left"""

					# use different length sentences to test batching
					__UpperCAmelCase   :      Tuple										=     [
					    """Hello, my dog is a little""",
					    """Today, I""",
					]

					__UpperCAmelCase   :      Optional[Any]										=     tokenizer(__UpperCAmelCase ,       return_tensors="""jax""" ,       padding=__UpperCAmelCase				)
					__UpperCAmelCase   :      Union[str, Any]										=     inputs["""input_ids"""]

					__UpperCAmelCase   :      str										=     model.generate(input_ids=__UpperCAmelCase ,       attention_mask=inputs["""attention_mask"""] ,       trace=__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[int]										=     tokenizer(sentences[0] ,       return_tensors="""jax"""				).input_ids
					__UpperCAmelCase   :      Optional[int]										=     model.generate(input_ids=__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
					__UpperCAmelCase   :      Any										=     tokenizer(sentences[1] ,       return_tensors="""jax"""				).input_ids
					__UpperCAmelCase   :      int										=     model.generate(input_ids=__UpperCAmelCase ,       max_length=model.config.max_length - num_paddings				)

					__UpperCAmelCase   :      List[Any]										=     tokenizer.batch_decode(outputs[0] ,       skip_special_tokens=__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     tokenizer.decode(output_non_padded[0][0] ,       skip_special_tokens=__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     tokenizer.decode(output_padded[0][0] ,       skip_special_tokens=__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[Any]										=     [
					    """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'''
from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims




