

'''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 			__UpperCamelCase							(		lowercase__  :					Dict,   lowercase__  :					List[str],   lowercase__  :					List[str]=None,   lowercase__  :					Union[str, Any]=None	):






     '''simple docstring'''





     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 					lowerCAmelCase       :







       def __init__(							self     :	List[Any]  , __lowercase     :	Dict  , __lowercase     :	Optional[Any]=13  , __lowercase     :	Optional[int]=7  , __lowercase     :	Dict=True  , __lowercase     :	str=False  , __lowercase     :	Any=99  , __lowercase     :	Tuple=16  , __lowercase     :	Optional[Any]=2  , __lowercase     :	str=4  , __lowercase     :	List[str]=4  , __lowercase     :	Union[str, Any]="gelu"  , __lowercase     :	List[Any]=0.1  , __lowercase     :	int=0.1  , __lowercase     :	Optional[Any]=20  , __lowercase     :	Any=2  , __lowercase     :	Optional[int]=1  , __lowercase     :	Dict=0  , __lowercase     :	int=16  , __lowercase     :	int=16  , __lowercase     :	int=0.0_2  , ):







            """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      snake_case				(							self     :	Dict ):







            """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=__lowercase  , word_embed_proj_dim=self.word_embed_proj_dim  , initializer_range=self.initializer_range  , use_cache=__lowercase  , )
            __lowercase     						=prepare_opt_inputs_dict(__lowercase  , __lowercase )
            return config, inputs_dict







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict  , __lowercase     :	Any  , __lowercase     :	Tuple  , __lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=20
            __lowercase     						=model_class_name(__lowercase )

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

            __lowercase     						=model.init_cache(input_ids.shape[0]  , __lowercase )
            __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=__lowercase  , past_key_values=__lowercase  , position_ids=__lowercase  , )

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

            __lowercase     						=model(__lowercase )

            __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      snake_case				(							self     :	int  , __lowercase     :	str  , __lowercase     :	Union[str, Any]  , __lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=20
            __lowercase     						=model_class_name(__lowercase )

            __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]  , __lowercase )
            __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=__lowercase  , past_key_values=__lowercase  , position_ids=__lowercase  , )
            __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=__lowercase  , position_ids=__lowercase  , )

            __lowercase     						=model(__lowercase  , attention_mask=__lowercase )

            __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 					lowerCAmelCase       ( A				,     unittest.TestCase				,     A				):
       lowerCAmelCase_				=	(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       lowerCAmelCase_				=	() if is_flax_available() else ()







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=FlaxOPTModelTester(self )







       def      snake_case				(							self     :	int ):







            """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(__lowercase  , __lowercase  , __lowercase )







       def      snake_case				(							self     :	List[str] ):







            """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(__lowercase  , __lowercase  , __lowercase )







       @slow
       def      snake_case				(							self     :	Any ):







            """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(__lowercase )
                 self.assertIsNotNone(__lowercase )






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







       @slow
       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=FlaxOPTModel.from_pretrained('facebook/opt-350m' )
            __lowercase     						=jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
            __lowercase     						=model(input_ids=__lowercase ).last_hidden_state
            __lowercase     						=(1, 11, 512)
            self.assertEqual(output.shape  , __lowercase )
            __lowercase     						=jnp.array(
                [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]] )
            self.assertTrue(jnp.allclose(output[:, :3, :3]  , __lowercase  , atol=4E-2 ) )






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







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """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(__lowercase  , return_tensors='jax'  , padding=__lowercase  , add_special_tokens=__lowercase )
            __lowercase     						=model(inputs.input_ids  , attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
            __lowercase     						=jnp.array(
                [
                    [1.3_8_5_1, -13.8923, -10.5229, -10.7533, -0.2_3_0_9, -10.2384, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                    [-4.7_0_7_3, -10.6276, -3.9_4_1_5, -21.5242, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                    [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -14.1650, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                    [6.4_7_8_3, -1.9_9_1_3, -10.7926, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
                ] )
            self.assertTrue(jnp.allclose(__lowercase  , __lowercase  , atol=4E-2 ) )

            __lowercase     						=jax.jit(__lowercase )
            __lowercase     						=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 					lowerCAmelCase       ( unittest.TestCase				):







       @property
       def      snake_case				(							self     :	str ):







            """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      snake_case				(							self     :	Tuple ):







            """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(__lowercase )
            __lowercase     						=GPTaTokenizer.from_pretrained(__lowercase )

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

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

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

            self.assertListEqual(__lowercase  , __lowercase )







       def      snake_case				(							self     :	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(__lowercase )
            __lowercase     						=GPTaTokenizer.from_pretrained(__lowercase )

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

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

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

            self.assertListEqual(__lowercase  , __lowercase )







       def      snake_case				(							self     :	List[str] ):







            """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(__lowercase )
            __lowercase     						=GPTaTokenizer.from_pretrained(__lowercase )
            __lowercase     						=tokenizer(
                [
                    'Today is a beautiful day and I want to',
                    'In the city of',
                ]  , return_tensors='jax'  , padding=__lowercase  , )

            __lowercase     						=jax.jit(model.generate )

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

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

            self.assertIsNotNone(__lowercase  , __lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						='facebook/opt-350m'

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

            __lowercase     						='left'

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

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

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

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

            __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=__lowercase  , max_length=model.config.max_length - num_paddings )

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

            __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(__lowercase  , __lowercase )
            self.assertListEqual(__lowercase  , [non_padded_sentence, padded_sentence] )





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase							=					{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTHybridForImageClassification''',
           '''ViTHybridModel''',
           '''ViTHybridPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
       from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit_hybrid import (
                  VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTHybridForImageClassification,
                  ViTHybridModel,
                  ViTHybridPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
       import sys

       UpperCAmelCase							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


