
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
							SCREAMING_SNAKE_CASE__							=    """platform"""

							import jax
							import jax.numpy as jnp

							from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				List[str]   ,							SCREAMING_SNAKE_CASE	:				Any=None   ,							SCREAMING_SNAKE_CASE	:				Dict=None      )					->						Any:
				if attention_mask is None:
								__lowercase								= 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  :       Any					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Dict=13					,			_UpperCAmelCase  :       List[Any]=7					,			_UpperCAmelCase  :       Optional[Any]=True					,			_UpperCAmelCase  :       List[str]=False					,			_UpperCAmelCase  :       int=99					,			_UpperCAmelCase  :       List[Any]=16					,			_UpperCAmelCase  :       List[Any]=2					,			_UpperCAmelCase  :       Optional[Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Optional[int]="gelu"					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       List[Any]=0.1					,			_UpperCAmelCase  :       Union[str, Any]=20					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       str=1					,			_UpperCAmelCase  :       Any=0					,			_UpperCAmelCase  :       Optional[Any]=16					,			_UpperCAmelCase  :       Optional[Any]=16					,			_UpperCAmelCase  :       List[Any]=0.02					,			)						->		Any:



											"""simple docstring"""


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





							def       a__			(			self  :       int    )						->		List[Any]:



											"""simple docstring"""


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

											__lowercase								= 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					,			)
											__lowercase								= prepare_opt_inputs_dict(_UpperCAmelCase					,			_UpperCAmelCase    )
											return config, inputs_dict





							def       a__			(			self  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase		, __lowercase								= self.prepare_config_and_inputs()
											return config, inputs_dict





							def       a__			(			self  :       Optional[Any]					,			_UpperCAmelCase  :       Union[str, Any]					,			_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       str    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= 20
											__lowercase								= model_class_name(_UpperCAmelCase    )

											__lowercase								= inputs_dict['input_ids']
											__lowercase								= inputs_dict['attention_mask']

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

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

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

											__lowercase								= model(_UpperCAmelCase    )

											__lowercase								= 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  :       Optional[int]					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Any    )						->		Tuple:



											"""simple docstring"""


											__lowercase								= 20
											__lowercase								= model_class_name(_UpperCAmelCase    )

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

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

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

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

											__lowercase								= model(_UpperCAmelCase					,			attention_mask=_UpperCAmelCase    )

											__lowercase								= 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__       (						lowerCAmelCase__   ,						unittest.TestCase   ,						lowerCAmelCase__    ):
							lowerCAmelCase__		:						str         =							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							lowerCAmelCase__		:						Optional[Any]         =							() if is_flax_available() else ()





							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= FlaxOPTModelTester(self    )





							def       a__			(			self  :       List[Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase		, __lowercase								= 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  :       Any    )						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase		, __lowercase								= 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  :       int    )						->		Tuple:



											"""simple docstring"""


											for model_class_name in self.all_model_classes:
															__lowercase								= model_class_name.from_pretrained('facebook/opt-125m'    )
															__lowercase								= np.ones((1, 1)    ) * model.config.eos_token_id
															__lowercase								= model(_UpperCAmelCase    )
															self.assertIsNotNone(_UpperCAmelCase    )






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





							@slow
							def       a__			(			self  :       Any    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= FlaxOPTModel.from_pretrained('facebook/opt-350m'    )
											__lowercase								= jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]    )
											__lowercase								= model(input_ids=_UpperCAmelCase    ).last_hidden_state
											__lowercase								= (1, 11, 5_12)
											self.assertEqual(output.shape					,			_UpperCAmelCase    )
											__lowercase								= 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      A__       (						unittest.TestCase    ):





							def       a__			(			self  :       List[Any]    )						->		List[str]:



											"""simple docstring"""


											super().setUp()
											__lowercase								= 'facebook/opt-350m'





							def       a__			(			self  :       List[Any]    )						->		str:



											"""simple docstring"""


											__lowercase								= FlaxOPTForCausalLM.from_pretrained(self.path_model    )
											__lowercase								= GPTaTokenizer.from_pretrained(self.path_model    )

											__lowercase								= [
											    '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
											__lowercase								= tokenizer(_UpperCAmelCase					,			return_tensors='jax'					,			padding=_UpperCAmelCase					,			add_special_tokens=_UpperCAmelCase    )
											__lowercase								= model(inputs.input_ids					,			attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
											__lowercase								= 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    )    )

											__lowercase								= jax.jit(_UpperCAmelCase    )
											__lowercase								= 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[str]    )						->		Optional[int]:



											"""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  :       Union[str, Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= 'facebook/opt-125m'

											__lowercase								= [
											    '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',
											]

											__lowercase								= []

											__lowercase								= FlaxOPTForCausalLM.from_pretrained(_UpperCAmelCase    )
											__lowercase								= GPTaTokenizer.from_pretrained(_UpperCAmelCase    )

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

															__lowercase								= model.generate(_UpperCAmelCase					,			max_length=10    )
															__lowercase								= generated_ids[0]

															__lowercase								= tokenizer.batch_decode(_UpperCAmelCase					,			skip_special_tokens=_UpperCAmelCase    )
															predicted_outputs += generated_string

											self.assertListEqual(_UpperCAmelCase					,			_UpperCAmelCase    )





							def       a__			(			self  :       Union[str, Any]    )						->		Dict:



											"""simple docstring"""


											__lowercase								= 'facebook/opt-350m'

											__lowercase								= [
											    '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',
											]

											__lowercase								= []
											__lowercase								= FlaxOPTForCausalLM.from_pretrained(_UpperCAmelCase    )
											__lowercase								= GPTaTokenizer.from_pretrained(_UpperCAmelCase    )

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

															__lowercase								= model.generate(_UpperCAmelCase					,			max_length=10    )
															__lowercase								= generated_ids[0]

															__lowercase								= tokenizer.batch_decode(_UpperCAmelCase					,			skip_special_tokens=_UpperCAmelCase    )
															predicted_outputs += generated_string

											self.assertListEqual(_UpperCAmelCase					,			_UpperCAmelCase    )





							def       a__			(			self  :       Dict    )						->		int:



											"""simple docstring"""


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

											__lowercase								= jax.jit(model.generate    )

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

											__lowercase								= tokenizer.batch_decode(_UpperCAmelCase					,			skip_special_tokens=_UpperCAmelCase    )

											self.assertIsNotNone(_UpperCAmelCase					,			_UpperCAmelCase    )





							def       a__			(			self  :       Union[str, Any]    )						->		Any:



											"""simple docstring"""


											__lowercase								= 'facebook/opt-350m'

											__lowercase								= GPTaTokenizer.from_pretrained(_UpperCAmelCase    )
											__lowercase								= FlaxOPTForCausalLM.from_pretrained(_UpperCAmelCase    )

											__lowercase								= 'left'

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

											__lowercase								= tokenizer(_UpperCAmelCase					,			return_tensors='jax'					,			padding=_UpperCAmelCase    )
											__lowercase								= inputs['input_ids']

											__lowercase								= model.generate(input_ids=_UpperCAmelCase					,			attention_mask=inputs['attention_mask']					,			trace=_UpperCAmelCase    )

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

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

											__lowercase								= tokenizer.batch_decode(outputs[0]					,			skip_special_tokens=_UpperCAmelCase    )
											__lowercase								= tokenizer.decode(output_non_padded[0][0]					,			skip_special_tokens=_UpperCAmelCase    )
											__lowercase								= tokenizer.decode(output_padded[0][0]					,			skip_special_tokens=_UpperCAmelCase    )

											__lowercase								= [
											    '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]    )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


											super().__init__(pad_token_id=_UpperCAmelCase					,			**_UpperCAmelCase    )

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





							@property
							def       a__			(			self  :       Any    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


											if self.task == "multiple-choice":
															__lowercase								= {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															__lowercase								= {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]    )





