

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
    lowerCamelCase_					  =   '''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def   UpperCamelCase(   lowercase_     ,   lowercase_     ,   lowercase_=None     ,   lowercase_=None				)      ->							Union[str, Any]:





  '''simple docstring'''





  if attention_mask is None:
    snake_case_					= 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				,    lowerCamelCase				,    lowerCamelCase=13				,    lowerCamelCase=7				,    lowerCamelCase=True				,    lowerCamelCase=False				,    lowerCamelCase=99				,    lowerCamelCase=16				,    lowerCamelCase=2				,    lowerCamelCase=4				,    lowerCamelCase=4				,    lowerCamelCase="gelu"				,    lowerCamelCase=0.1				,    lowerCamelCase=0.1				,    lowerCamelCase=20				,    lowerCamelCase=2				,    lowerCamelCase=1				,    lowerCamelCase=0				,    lowerCamelCase=16				,    lowerCamelCase=16				,    lowerCamelCase=0.02				,    )		->			List[Any]:
        snake_case_					= parent
        snake_case_					= batch_size
        snake_case_					= seq_length
        snake_case_					= is_training
        snake_case_					= use_labels
        snake_case_					= vocab_size
        snake_case_					= hidden_size
        snake_case_					= num_hidden_layers
        snake_case_					= num_attention_heads
        snake_case_					= intermediate_size
        snake_case_					= hidden_act
        snake_case_					= hidden_dropout_prob
        snake_case_					= attention_probs_dropout_prob
        snake_case_					= max_position_embeddings
        snake_case_					= eos_token_id
        snake_case_					= pad_token_id
        snake_case_					= bos_token_id
        snake_case_					= embed_dim
        snake_case_					= word_embed_proj_dim
        snake_case_					= initializer_range
        snake_case_					= False







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        snake_case_					= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,    self.vocab_size  )				,    3				,    self.vocab_size  )
        snake_case_					= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,    dtype=np.intaa  ))				,    -1  )

        snake_case_					= 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=lowerCamelCase				,    word_embed_proj_dim=self.word_embed_proj_dim				,    initializer_range=self.initializer_range				,    use_cache=lowerCamelCase				,    )
        snake_case_					= prepare_opt_inputs_dict(lowerCamelCase				,    lowerCamelCase  )
        return config, inputs_dict







      def       lowerCAmelCase_       (	self  )		->			Dict:
        snake_case_		,							snake_case_					= self.prepare_config_and_inputs()
        return config, inputs_dict







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			str:
        snake_case_					= 20
        snake_case_					= model_class_name(lowerCamelCase  )

        snake_case_					= inputs_dict["""input_ids"""]
        snake_case_					= inputs_dict["""attention_mask"""]

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

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

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

        snake_case_					= model(lowerCamelCase  )

        snake_case_					= 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       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			str:
        snake_case_					= 20
        snake_case_					= model_class_name(lowerCamelCase  )

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

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

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

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

        snake_case_					= model(lowerCamelCase				,    attention_mask=lowerCamelCase  )

        snake_case_					= 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							(      __snake_case    ,				unittest.TestCase    ,				__snake_case      ):
      lowerCamelCase_      : Optional[int]  			=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCamelCase_      : Tuple  			=   () if is_flax_available() else ()







      def       lowerCAmelCase_       (	self  )		->			str:
        snake_case_					= FlaxOPTModelTester(self  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_		,							snake_case_					= self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			str:
        snake_case_		,							snake_case_					= 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(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )







      @slow
      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        for model_class_name in self.all_model_classes:
          snake_case_					= model_class_name.from_pretrained("""facebook/opt-125m"""  )
          snake_case_					= np.ones((1, 1)  ) * model.config.eos_token_id
          snake_case_					= model(lowerCamelCase  )
          self.assertIsNotNone(lowerCamelCase  )







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







      @slow
      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= FlaxOPTModel.from_pretrained("""facebook/opt-350m"""  )
        snake_case_					= jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
        snake_case_					= model(input_ids=lowerCamelCase  ).last_hidden_state
        snake_case_					= (1, 11, 512)
        self.assertEqual(output.shape				,    lowerCamelCase  )
        snake_case_					= 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]				,    lowerCamelCase				,    atol=4e-2  )  )







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







      def       lowerCAmelCase_       (	self  )		->			Dict:
        super().setUp()
        snake_case_					= """facebook/opt-350m"""







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        snake_case_					= FlaxOPTForCausalLM.from_pretrained(self.path_model  )
        snake_case_					= GPTaTokenizer.from_pretrained(self.path_model  )

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

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







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







      @property
      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        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       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= """facebook/opt-125m"""

        snake_case_					= [
            """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""",
        ]

        snake_case_					= []

        snake_case_					= FlaxOPTForCausalLM.from_pretrained(lowerCamelCase  )
        snake_case_					= GPTaTokenizer.from_pretrained(lowerCamelCase  )

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

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

          snake_case_					= tokenizer.batch_decode(lowerCamelCase				,    skip_special_tokens=lowerCamelCase  )
          predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        snake_case_					= """facebook/opt-350m"""

        snake_case_					= [
            """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""",
        ]

        snake_case_					= []
        snake_case_					= FlaxOPTForCausalLM.from_pretrained(lowerCamelCase  )
        snake_case_					= GPTaTokenizer.from_pretrained(lowerCamelCase  )

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

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

          snake_case_					= tokenizer.batch_decode(lowerCamelCase				,    skip_special_tokens=lowerCamelCase  )
          predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        snake_case_					= """facebook/opt-125m"""
        snake_case_					= [
            """Today is a beautiful day and I want to thank""",
            """In the city of Rome Canaver Canaver Canaver Canaver""",
        ]
        snake_case_					= FlaxOPTForCausalLM.from_pretrained(lowerCamelCase  )
        snake_case_					= GPTaTokenizer.from_pretrained(lowerCamelCase  )
        snake_case_					= tokenizer(
            [
                """Today is a beautiful day and I want to""",
                """In the city of""",
            ]				,    return_tensors="""jax"""				,    padding=lowerCamelCase				,    )

        snake_case_					= jax.jit(model.generate  )

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

        snake_case_					= tokenizer.batch_decode(lowerCamelCase				,    skip_special_tokens=lowerCamelCase  )

        self.assertIsNotNone(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= """facebook/opt-350m"""

        snake_case_					= GPTaTokenizer.from_pretrained(lowerCamelCase  )
        snake_case_					= FlaxOPTForCausalLM.from_pretrained(lowerCamelCase  )

        snake_case_					= """left"""

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

        snake_case_					= tokenizer(lowerCamelCase				,    return_tensors="""jax"""				,    padding=lowerCamelCase  )
        snake_case_					= inputs["""input_ids"""]

        snake_case_					= model.generate(input_ids=lowerCamelCase				,    attention_mask=inputs["""attention_mask"""]				,    trace=lowerCamelCase  )

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

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

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

        snake_case_					= [
            """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(lowerCamelCase				,    lowerCamelCase  )
        self.assertListEqual(lowerCamelCase				,    [non_padded_sentence, padded_sentence]  )


def   UpperCamelCase(   lowercase_				)      ->							str:





  '''simple docstring'''





  return "".join(chr(ord(lowercase_				) + 32				) if """A""" <= char <= """Z""" else char for char in word				)


if __name__ == "__main__":
    from doctest import testmod

    testmod()