



'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Dict ,					UpperCAmelCase__ :Dict ,					UpperCAmelCase__ :Optional[int]=None ,					UpperCAmelCase__ :Tuple=None					):






 '''simple docstring'''

 if attention_mask is None:
  a						=		tf.cast(tf.math.not_equal(UpperCAmelCase__ ,					config.pad_token_id					) ,					tf.inta					)
 return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 		_lowercase		:
   _UpperCAmelCase     	=			OPTConfig
   _UpperCAmelCase     	=			{}
   _UpperCAmelCase     	=			'''gelu'''

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


    """simple docstring"""






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

   def 		A							(      self			:       Optional[int]							)       ->				Any:


    """simple docstring"""






    a						=		ids_tensor([self.batch_size, self.seq_length - 1]							,   self.vocab_size							)
    a						=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)							,   1							)
    a						=		tf.concat([input_ids, eos_tensor]							,   axis=1							)

    a						=		self.config_cls(
        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							,   word_embed_proj_dim=self.word_embed_proj_dim							,   is_encoder_decoder=__lowerCAmelCase							,   **self.config_updates							,   )
    a						=		prepare_opt_inputs_dict(__lowerCAmelCase							,   __lowerCAmelCase							)
    return config, inputs_dict




   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    a						=		TFOPTModel(config=__lowerCAmelCase							)
    a						=		inputs_dict["input_ids"]

    a						=		input_ids[:1, :]
    a						=		inputs_dict["attention_mask"][:1, :]
    a						=		1

    # first forward pass
    a						=		model(__lowerCAmelCase							,   attention_mask=__lowerCAmelCase							,   use_cache=__lowerCAmelCase							)

    a		,  a						=		outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    a						=		ids_tensor((self.batch_size, 3)							,   config.vocab_size							)
    a						=		tf.cast(ids_tensor((self.batch_size, 3)							,   2							)							,   tf.inta							)

    # append to next input_ids and
    a						=		tf.concat([input_ids, next_tokens]							,   axis=-1							)
    a						=		tf.concat([attention_mask, next_attn_mask]							,   axis=-1							)

    a						=		model(__lowerCAmelCase							,   attention_mask=__lowerCAmelCase							)[0]
    a						=		model(__lowerCAmelCase							,   attention_mask=__lowerCAmelCase							,   past_key_values=__lowerCAmelCase							)[0]

    self.parent.assertEqual(next_tokens.shape[1]							,   output_from_past.shape[1]							)

    # select random slice
    a						=		int(ids_tensor((1,)							,   output_from_past.shape[-1]							)							)
    a						=		output_from_no_past[:, -3:, random_slice_idx]
    a						=		output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(__lowerCAmelCase							,   __lowerCAmelCase							,   rtol=1E-3							)



@require_tf
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
   _UpperCAmelCase     	=			(TFOPTForCausalLM,) if is_tf_available() else ()
   _UpperCAmelCase     	=			(
       {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
   )
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			10

   def 		A							(      self			:       int							)       ->				List[str]:


    """simple docstring"""






    a						=		TFOPTModelTester(self							)
    a						=		ConfigTester(self							,   config_class=__lowerCAmelCase							)

   def 		A							(      self			:       str							)       ->				Optional[int]:


    """simple docstring"""






    self.config_tester.run_common_tests()

   def 		A							(      self			:       int							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase							)




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


    """simple docstring"""






    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

    def _get_word_embedding_weight(__lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Dict							):
     if hasattr(__lowerCAmelCase							,   "weight"							):
      return embedding_layer.weight
     else:
      # Here we build the word embeddings weights if not exists.
      # And then we retry to get the attribute once built.
      model.build()
      if hasattr(__lowerCAmelCase							,   "weight"							):
       return embedding_layer.weight
      else:
       return None

    for model_class in self.all_model_classes:
     for size in [config.vocab_size - 10, config.vocab_size + 10]:
      # build the embeddings
      a						=		model_class(config=__lowerCAmelCase							)
      a						=		_get_word_embedding_weight(__lowerCAmelCase							,   model.get_input_embeddings()							)
      a						=		_get_word_embedding_weight(__lowerCAmelCase							,   model.get_output_embeddings()							)

      # reshape the embeddings
      model.resize_token_embeddings(__lowerCAmelCase							)
      a						=		_get_word_embedding_weight(__lowerCAmelCase							,   model.get_input_embeddings()							)
      a						=		_get_word_embedding_weight(__lowerCAmelCase							,   model.get_output_embeddings()							)

      # check that the resized embeddings size matches the desired size.
      a						=		size if size is not None else config.vocab_size

      self.assertEqual(new_input_embeddings.shape[0]							,   __lowerCAmelCase							)

      # check that weights remain the same after resizing
      a						=		True
      for pa, pa in zip(old_input_embeddings.value()							,   new_input_embeddings.value()							):
       if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
        a						=		False
      self.assertTrue(__lowerCAmelCase							)

      if old_output_embeddings is not None and new_output_embeddings is not None:
       self.assertEqual(new_output_embeddings.shape[0]							,   __lowerCAmelCase							)

       a						=		True
       for pa, pa in zip(old_output_embeddings.value()							,   new_output_embeddings.value()							):
        if tf.math.reduce_sum(tf.math.abs(pa - pa							)							) > 0:
         a						=		False
       self.assertTrue(__lowerCAmelCase							)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, Any]					):






 '''simple docstring'''

 return tf.constant(UpperCAmelCase__ ,					dtype=tf.intaa					)



@require_tf
class 		_lowercase		(       unittest.TestCase	):
   _UpperCAmelCase     	=			99

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


    """simple docstring"""






    a						=		tf.ones((4, 1)							,   dtype=tf.intaa							) * 2
    a						=		tf.concat([ids_tensor((4, 6)							,   self.vocab_size - 3							) + 3, eos_column_vector]							,   axis=1							)
    a						=		input_ids.shape[0]
    a						=		OPTConfig(
        vocab_size=self.vocab_size							,   hidden_size=24							,   num_hidden_layers=2							,   num_attention_heads=2							,   ffn_dim=32							,   max_position_embeddings=48							,   eos_token_id=2							,   pad_token_id=1							,   bos_token_id=0							,   )
    return config, input_ids, batch_size



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

   @slow
   def 		A							(      self			:       Any							)       ->				Tuple:


    """simple docstring"""






    a						=		TFOPTModel.from_pretrained("facebook/opt-350m"							)
    a						=		_long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]							)
    a						=		tf.not_equal(__lowerCAmelCase							,   model.config.pad_token_id							)
    with tf.GradientTape():
     a						=		model(input_ids=__lowerCAmelCase							,   attention_mask=__lowerCAmelCase							).last_hidden_state
    a						=		(1, 11, 512)
    self.assertEqual(output.shape							,   __lowerCAmelCase							)
    a						=		tf.constant(
        [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]							)
    self.assertTrue(np.allclose(output[:, :3, :3]							,   __lowerCAmelCase							,   atol=4E-3							)							)

    a						=		tf.function(__lowerCAmelCase							,   jit_compile=__lowerCAmelCase							)
    a						=		xla_generate(__lowerCAmelCase							,   __lowerCAmelCase							)[0]
    self.assertTrue(np.allclose(output[:, :3, :3]							,   __lowerCAmelCase							,   atol=4E-2							)							)



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

   def 		A							(      self			:       Optional[Any]							)       ->				Optional[Any]:


    """simple docstring"""






    super().setUp()
    a						=		"facebook/opt-350m"




   def 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		TFOPTForCausalLM.from_pretrained(self.path_model							)
    a						=		GPTaTokenizer.from_pretrained(self.path_model							)

    a						=		[
        "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
    a						=		tokenizer(__lowerCAmelCase							,   return_tensors="tf"							,   padding=__lowerCAmelCase							,   add_special_tokens=__lowerCAmelCase							)
    a						=		tf.math.reduce_mean(model(inputs.input_ids							,   attention_mask=inputs.attention_mask							)[0]							,   axis=-1							)
    a						=		tf.constant(
        [
            [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(np.allclose(__lowerCAmelCase							,   __lowerCAmelCase							,   atol=1E-4							)							)

    a						=		tf.function(__lowerCAmelCase							,   jit_compile=__lowerCAmelCase							)
    a						=		tf.math.reduce_mean(xla_generate(inputs.input_ids							,   attention_mask=inputs.attention_mask							)[0]							,   axis=-1							)
    self.assertTrue(np.allclose(__lowerCAmelCase							,   __lowerCAmelCase							,   atol=1E-4							)							)



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

   @property
   def 		A							(      self			:       Dict							)       ->				List[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 		A							(      self			:       Dict							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		"facebook/opt-125m"

    a						=		[
        "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",
    ]

    a						=		[]
    a						=		GPTaTokenizer.from_pretrained(__lowerCAmelCase							)
    a						=		TFOPTForCausalLM.from_pretrained(__lowerCAmelCase							)

    for prompt in self.prompts:
     a						=		tokenizer(__lowerCAmelCase							,   return_tensors="tf"							).input_ids

     a						=		model.generate(__lowerCAmelCase							,   max_length=10							)

     a						=		tokenizer.batch_decode(__lowerCAmelCase							,   skip_special_tokens=__lowerCAmelCase							)
     predicted_outputs += generated_string

    self.assertListEqual(__lowerCAmelCase							,   __lowerCAmelCase							)

   def 		A							(      self			:       Any							)       ->				Dict:


    """simple docstring"""






    a						=		"facebook/opt-350m"

    a						=		GPTaTokenizer.from_pretrained(__lowerCAmelCase							)
    a						=		TFOPTForCausalLM.from_pretrained(__lowerCAmelCase							)

    a						=		"left"

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

    a						=		tokenizer(__lowerCAmelCase							,   return_tensors="tf"							,   padding=__lowerCAmelCase							)
    a						=		inputs["input_ids"]

    a						=		model.generate(input_ids=__lowerCAmelCase							,   attention_mask=inputs["attention_mask"]							)

    a						=		tokenizer(sentences[0]							,   return_tensors="tf"							).input_ids
    a						=		model.generate(input_ids=__lowerCAmelCase							)

    a						=		inputs_non_padded.shape[-1] - tf.math.reduce_sum(
        tf.cast(inputs["attention_mask"][-1]							,   tf.intaa							)							)
    a						=		tokenizer(sentences[1]							,   return_tensors="tf"							).input_ids
    a						=		model.generate(input_ids=__lowerCAmelCase							,   max_length=model.config.max_length - num_paddings							)

    a						=		tokenizer.batch_decode(__lowerCAmelCase							,   skip_special_tokens=__lowerCAmelCase							)
    a						=		tokenizer.decode(output_non_padded[0]							,   skip_special_tokens=__lowerCAmelCase							)
    a						=		tokenizer.decode(output_padded[0]							,   skip_special_tokens=__lowerCAmelCase							)

    a						=		[
        "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 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		"facebook/opt-350m"

    a						=		[
        "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",
    ]

    a						=		[]
    a						=		GPTaTokenizer.from_pretrained(__lowerCAmelCase							)
    a						=		TFOPTForCausalLM.from_pretrained(__lowerCAmelCase							)

    for prompt in self.prompts:
     a						=		tokenizer(__lowerCAmelCase							,   return_tensors="tf"							).input_ids

     a						=		model.generate(__lowerCAmelCase							,   max_length=10							)

     a						=		tokenizer.batch_decode(__lowerCAmelCase							,   skip_special_tokens=__lowerCAmelCase							)
     predicted_outputs += generated_string

    self.assertListEqual(__lowerCAmelCase							,   __lowerCAmelCase							)






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt



class 		_lowercase		:

   def __init__(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






    a						=		""
    a						=		""
    a						=		[]
    a						=		0
    a						=		256
    a						=		0
    a						=		0
    a						=		0
    a						=		0

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Any							)       ->				int:


    """simple docstring"""






    a						=		cva.imread(__lowerCAmelCase							,   0							)
    a						=		copy.deepcopy(self.img							)
    a		,  a		,  a						=		plt.hist(self.img.ravel()							,   256							,   [0, 256]							,   label="x"							)
    a						=		np.sum(__lowerCAmelCase							)
    for i in range(len(__lowerCAmelCase							)							):
     a						=		x[i] / self.k
     self.sk += prk
     a						=		(self.L - 1) * self.sk
     if self.rem != 0:
      a						=		int(last % last							)
     a						=		int(last + 1 if self.rem >= 0.5 else last							)
     self.last_list.append(__lowerCAmelCase							)
     a						=		int(np.ma.count(self.img							) / self.img[1].size							)
     a						=		self.img[1].size
    for i in range(self.number_of_cols							):
     for j in range(self.number_of_rows							):
      a						=		self.img[j][i]
      if num != self.last_list[num]:
       a						=		self.last_list[num]
    cva.imwrite("output_data/output.jpg"							,   self.img							)

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    plt.hist(self.img.ravel()							,   256							,   [0, 256]							)




   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    cva.imshow("Output-Image"							,   self.img							)
    cva.imshow("Input-Image"							,   self.original_image							)
    cva.waitKey(5000							)
    cva.destroyAllWindows()


if __name__ == "__main__":
     A_		:     List[Any]        =     os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     A_		:     int        =     ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()

