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_    :    Any											=						"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def 					a__ (						UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     Any=None						,     UpperCAmelCase__    :     Dict=None     )->		Union[str, Any]:
						if attention_mask is None:
												UpperCAmelCase__   		=						np.where(input_ids != config.pad_token_id						,     1						,     0     )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						}


@require_flax
class      _lowercase						:

				def __init__(       self    :      Dict    ,      _lowercase    :      List[str]    ,      _lowercase    :      Optional[Any]=1_3    ,      _lowercase    :      Union[str, Any]=7    ,      _lowercase    :      Dict=True    ,      _lowercase    :      Any=False    ,      _lowercase    :      int=9_9    ,      _lowercase    :      Any=1_6    ,      _lowercase    :      Optional[Any]=2    ,      _lowercase    :      Optional[int]=4    ,      _lowercase    :      Union[str, Any]=4    ,      _lowercase    :      List[Any]="gelu"    ,      _lowercase    :      List[str]=0.1    ,      _lowercase    :      str=0.1    ,      _lowercase    :      str=2_0    ,      _lowercase    :      Optional[Any]=2    ,      _lowercase    :      Dict=1    ,      _lowercase    :      Optional[Any]=0    ,      _lowercase    :      Tuple=1_6    ,      _lowercase    :      Optional[Any]=1_6    ,      _lowercase    :      List[Any]=0.02    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						parent
										UpperCAmelCase__   		=						batch_size
										UpperCAmelCase__   		=						seq_length
										UpperCAmelCase__   		=						is_training
										UpperCAmelCase__   		=						use_labels
										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						hidden_size
										UpperCAmelCase__   		=						num_hidden_layers
										UpperCAmelCase__   		=						num_attention_heads
										UpperCAmelCase__   		=						intermediate_size
										UpperCAmelCase__   		=						hidden_act
										UpperCAmelCase__   		=						hidden_dropout_prob
										UpperCAmelCase__   		=						attention_probs_dropout_prob
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						eos_token_id
										UpperCAmelCase__   		=						pad_token_id
										UpperCAmelCase__   		=						bos_token_id
										UpperCAmelCase__   		=						embed_dim
										UpperCAmelCase__   		=						word_embed_proj_dim
										UpperCAmelCase__   		=						initializer_range
										UpperCAmelCase__   		=						False

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,      self.vocab_size       )    ,      3    ,      self.vocab_size       )
										UpperCAmelCase__   		=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,      dtype=np.intaa       ))    ,      -1       )

										UpperCAmelCase__   		=						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=_lowercase    ,      word_embed_proj_dim=self.word_embed_proj_dim    ,      initializer_range=self.initializer_range    ,      use_cache=_lowercase    ,      )
										UpperCAmelCase__   		=						prepare_opt_inputs_dict(_lowercase    ,      _lowercase       )
										return config, inputs_dict

				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.prepare_config_and_inputs()
										return config, inputs_dict

				def     A	(       self    :      Any    ,      _lowercase    :      List[str]    ,      _lowercase    :      Optional[int]    ,      _lowercase    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						2_0
										UpperCAmelCase__   		=						model_class_name(_lowercase       )

										UpperCAmelCase__   		=						inputs_dict["""input_ids"""]
										UpperCAmelCase__   		=						inputs_dict["""attention_mask"""]

										UpperCAmelCase__   		=						model.init_cache(input_ids.shape[0]    ,      _lowercase       )
										UpperCAmelCase__   		=						jnp.ones((input_ids.shape[0], max_length)    ,      dtype="""i4"""       )

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

										UpperCAmelCase__   		=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype="""i4"""       )
										UpperCAmelCase__   		=						model(
										    input_ids[:, -1:]    ,      attention_mask=_lowercase    ,      past_key_values=outputs_cache.past_key_values    ,      position_ids=_lowercase    ,      )

										UpperCAmelCase__   		=						model(_lowercase       )

										UpperCAmelCase__   		=						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    :      Dict    ,      _lowercase    :      Dict    ,      _lowercase    :      Union[str, Any]    ,      _lowercase    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						2_0
										UpperCAmelCase__   		=						model_class_name(_lowercase       )

										UpperCAmelCase__		, UpperCAmelCase__   		=						(
										    inputs_dict["""input_ids"""],
										    inputs_dict["""attention_mask"""],
										)

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

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

										UpperCAmelCase__   		=						model(
										    input_ids[:, :-1]    ,      attention_mask=_lowercase    ,      past_key_values=_lowercase    ,      position_ids=_lowercase    ,      )
										UpperCAmelCase__   		=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype="""i4"""       )
										UpperCAmelCase__   		=						model(
										    input_ids[:, -1:]    ,      past_key_values=outputs_cache.past_key_values    ,      attention_mask=_lowercase    ,      position_ids=_lowercase    ,      )

										UpperCAmelCase__   		=						model(_lowercase    ,      attention_mask=_lowercase       )

										UpperCAmelCase__   		=						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					):
				lowerCamelCase__									=    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				lowerCamelCase__									=    () if is_flax_available() else ()

				def     A	(       self    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						FlaxOPTModelTester(self       )

				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(_lowercase    ,      _lowercase    ,      _lowercase       )

				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						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(_lowercase    ,      _lowercase    ,      _lowercase       )

				@slow
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										for model_class_name in self.all_model_classes:
																UpperCAmelCase__   		=						model_class_name.from_pretrained("""facebook/opt-125m"""       )
																UpperCAmelCase__   		=						np.ones((1, 1)       ) * model.config.eos_token_id
																UpperCAmelCase__   		=						model(_lowercase       )
																self.assertIsNotNone(_lowercase       )


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

				@slow
				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						FlaxOPTModel.from_pretrained("""facebook/opt-350m"""       )
										UpperCAmelCase__   		=						jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       )
										UpperCAmelCase__   		=						model(input_ids=_lowercase       ).last_hidden_state
										UpperCAmelCase__   		=						(1, 1_1, 5_1_2)
										self.assertEqual(output.shape    ,      _lowercase       )
										UpperCAmelCase__   		=						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]    ,      _lowercase    ,      atol=4e-2       )       )


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

				def     A	(       self    :      Optional[int]       ):


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

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						FlaxOPTForCausalLM.from_pretrained(self.path_model       )
										UpperCAmelCase__   		=						GPTaTokenizer.from_pretrained(self.path_model       )

										UpperCAmelCase__   		=						[
										    """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__   		=						tokenizer(_lowercase    ,      return_tensors="""jax"""    ,      padding=_lowercase    ,      add_special_tokens=_lowercase       )
										UpperCAmelCase__   		=						model(inputs.input_ids    ,      attention_mask=inputs.attention_mask       )[0].mean(axis=-1       )
										UpperCAmelCase__   		=						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(_lowercase    ,      _lowercase    ,      atol=4e-2       )       )

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


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

				@property
				def     A	(       self    :      Tuple       ):


										'''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    :      str       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""facebook/opt-125m"""

										UpperCAmelCase__   		=						[
										    """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__   		=						[]

										UpperCAmelCase__   		=						FlaxOPTForCausalLM.from_pretrained(_lowercase       )
										UpperCAmelCase__   		=						GPTaTokenizer.from_pretrained(_lowercase       )

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

																UpperCAmelCase__   		=						model.generate(_lowercase    ,      max_length=1_0       )
																UpperCAmelCase__   		=						generated_ids[0]

																UpperCAmelCase__   		=						tokenizer.batch_decode(_lowercase    ,      skip_special_tokens=_lowercase       )
																predicted_outputs += generated_string

										self.assertListEqual(_lowercase    ,      _lowercase       )

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""facebook/opt-350m"""

										UpperCAmelCase__   		=						[
										    """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__   		=						[]
										UpperCAmelCase__   		=						FlaxOPTForCausalLM.from_pretrained(_lowercase       )
										UpperCAmelCase__   		=						GPTaTokenizer.from_pretrained(_lowercase       )

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

																UpperCAmelCase__   		=						model.generate(_lowercase    ,      max_length=1_0       )
																UpperCAmelCase__   		=						generated_ids[0]

																UpperCAmelCase__   		=						tokenizer.batch_decode(_lowercase    ,      skip_special_tokens=_lowercase       )
																predicted_outputs += generated_string

										self.assertListEqual(_lowercase    ,      _lowercase       )

				def     A	(       self    :      Optional[Any]       ):


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

										UpperCAmelCase__   		=						jax.jit(model.generate       )

										UpperCAmelCase__   		=						jit_generate(inputs["""input_ids"""]    ,      attention_mask=inputs["""attention_mask"""]       ).sequences

										UpperCAmelCase__   		=						tokenizer.batch_decode(_lowercase    ,      skip_special_tokens=_lowercase       )

										self.assertIsNotNone(_lowercase    ,      _lowercase       )

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""facebook/opt-350m"""

										UpperCAmelCase__   		=						GPTaTokenizer.from_pretrained(_lowercase       )
										UpperCAmelCase__   		=						FlaxOPTForCausalLM.from_pretrained(_lowercase       )

										UpperCAmelCase__   		=						"""left"""

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

										UpperCAmelCase__   		=						tokenizer(_lowercase    ,      return_tensors="""jax"""    ,      padding=_lowercase       )
										UpperCAmelCase__   		=						inputs["""input_ids"""]

										UpperCAmelCase__   		=						model.generate(input_ids=_lowercase    ,      attention_mask=inputs["""attention_mask"""]    ,      trace=_lowercase       )

										UpperCAmelCase__   		=						tokenizer(sentences[0]    ,      return_tensors="""jax"""       ).input_ids
										UpperCAmelCase__   		=						model.generate(input_ids=_lowercase       )

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

										UpperCAmelCase__   		=						tokenizer.batch_decode(outputs[0]    ,      skip_special_tokens=_lowercase       )
										UpperCAmelCase__   		=						tokenizer.decode(output_non_padded[0][0]    ,      skip_special_tokens=_lowercase       )
										UpperCAmelCase__   		=						tokenizer.decode(output_padded[0][0]    ,      skip_special_tokens=_lowercase       )

										UpperCAmelCase__   		=						[
										    """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(_lowercase    ,      _lowercase       )
										self.assertListEqual(_lowercase    ,      [non_padded_sentence, padded_sentence]       )


"""simple docstring"""







import safs


class      _lowercase						(				safs.SaFileSystem					):
				pass

