
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
    import tensorflow as tf

if is_keras_nlp_available():
    from transformers.models.gpta import TFGPTaTokenizer


lowerCamelCase_						     =					['''gpt2''']
lowerCamelCase_						     =					'''gpt2'''

if is_tf_available():




    class      __A(    tf.Module			):

      """simple docstring"""







      def __init__(self	,      SCREAMING_SNAKE_CASE_						):
           super().__init__()
           UpperCamelCase__				=    tokenizer
           UpperCamelCase__				=    AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
           UpperCamelCase__				=    TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE_						)







      @tf.function(input_signature=(tf.TensorSpec((None,)	,      tf.string	,      name="""text"""						),)						)
      def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
           UpperCamelCase__				=    self.tokenizer(SCREAMING_SNAKE_CASE_						)
           UpperCamelCase__				=    tokenized["""input_ids"""].to_tensor()

           UpperCamelCase__				=    tf.cast(input_ids_dense > 0	,      tf.intaa						)
           # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

           UpperCamelCase__				=    self.model(input_ids=SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)["""logits"""]

           return outputs




@require_tf
@require_keras_nlp
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       super().setUp()

       UpperCamelCase__				=    [GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_						) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       UpperCamelCase__				=    [TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_						) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers						) == len(self.tf_tokenizers						)

       UpperCamelCase__				=    [
           """This is a straightforward English test sentence.""",
           """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
           """Now we're going to add some Chinese: 一 二 三 一二三""",
           """And some much more rare Chinese: 齉 堃 齉堃""",
           """Je vais aussi écrire en français pour tester les accents""",
           """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
       ]
       UpperCamelCase__				=    list(zip(self.test_sentences	,      self.test_sentences[::-1]						)						)







  def 	UpperCAmelCase_ (self						):
       for tokenizer, tf_tokenizer in zip(self.tokenizers	,      self.tf_tokenizers						):
            for test_inputs in self.test_sentences:
                 UpperCamelCase__				=    tokenizer([test_inputs]	,      return_tensors="""tf"""						)
                 UpperCamelCase__				=    tf_tokenizer([test_inputs]						)

                 for key in python_outputs.keys():
                      # convert them to numpy to avoid messing with ragged tensors
                      UpperCamelCase__				=    python_outputs[key].numpy()
                      UpperCamelCase__				=    tf_outputs[key].numpy()

                      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape						)						)
                      self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE_	,      tf.intaa						) == tf_outputs_values						)						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for tf_tokenizer in self.tf_tokenizers:
            UpperCamelCase__				=    tf.function(SCREAMING_SNAKE_CASE_						)
            for test_inputs in self.test_sentences:
                 UpperCamelCase__				=    tf.constant(SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    compiled_tokenizer(SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    tf_tokenizer(SCREAMING_SNAKE_CASE_						)

                 for key in eager_outputs.keys():
                      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for tf_tokenizer in self.tf_tokenizers:
            UpperCamelCase__				=    ModelToSave(tokenizer=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    tf.convert_to_tensor([self.test_sentences[0]]						)
            UpperCamelCase__				=    model.serving(SCREAMING_SNAKE_CASE_						)  # Build model with some sample inputs
            with TemporaryDirectory() as tempdir:
                 UpperCamelCase__				=    Path(SCREAMING_SNAKE_CASE_						) / """saved.model"""
                 tf.saved_model.save(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      signatures={"""serving_default""": model.serving}						)
                 UpperCamelCase__				=    tf.saved_model.load(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    loaded_model.signatures["""serving_default"""](SCREAMING_SNAKE_CASE_						)["""output_0"""]
            # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
            self.assertTrue(tf.reduce_all(out == loaded_output						)						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for tf_tokenizer in self.tf_tokenizers:
            UpperCamelCase__				=    tf.convert_to_tensor([self.test_sentences[0]]						)
            UpperCamelCase__				=    tf_tokenizer(SCREAMING_SNAKE_CASE_						)  # Build model with some sample inputs

            UpperCamelCase__				=    tf_tokenizer.get_config()
            UpperCamelCase__				=    TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model_from_config(SCREAMING_SNAKE_CASE_						)

            for key in from_config_output.keys():
                 self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]						)						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for tf_tokenizer in self.tf_tokenizers:
            # for the test to run
            UpperCamelCase__				=    12_31_23

            for max_length in [3, 5, 10_24]:
                 UpperCamelCase__				=    tf.convert_to_tensor([self.test_sentences[0]]						)
                 UpperCamelCase__				=    tf_tokenizer(SCREAMING_SNAKE_CASE_	,      max_length=SCREAMING_SNAKE_CASE_						)

                 UpperCamelCase__				=    out["""input_ids"""].numpy().shape[1]

                 assert out_length == max_length







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    R"""\w+[.]\d+"""
     UpperCamelCase__				=    re.findall(__a	,		__a						)
     for pat in pats:
          UpperCamelCase__				=    key.replace(__a	,		"""_""".join(pat.split("""."""						)						)						)
     return key






def 						__magic_name__			(   __a      :	str	,		__a      :	Dict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
     if (
         any("""norm""" in str_ for str_ in pt_tuple_key						)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""embedding""",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCamelCase__				=    pt_tensor.transpose(2	,		3	,		1	,		0						)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight":
          UpperCamelCase__				=    pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""weight""",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""bias""",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[Any]	,		__a      :	Optional[int]=42						):



     '''simple docstring'''




     UpperCamelCase__				=    {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCamelCase__				=    flax_model.init_weights(PRNGKey(__a						)						)

     UpperCamelCase__				=    flatten_dict(__a						)
     UpperCamelCase__				=    {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCamelCase__				=    rename_key(__a						)
          UpperCamelCase__				=    tuple(renamed_pt_key.split("""."""						)						)

          # Correctly rename weight parameters
          UpperCamelCase__		,	UpperCamelCase__				=    rename_key_and_reshape_tensor(__a	,		__a	,		__a						)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."						)

        # also add unexpected weight so that warning is thrown
          UpperCamelCase__				=    jnp.asarray(__a						)

     return unflatten_dict(__a						)





