






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



@require_tf
class 				_UpperCamelCase      :
      snake_case_	=				OPTConfig
      snake_case_	=				{}
      snake_case_	=				"""gelu"""


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=1_3       ,    __UpperCamelCase=7       ,    __UpperCamelCase=True       ,    __UpperCamelCase=False       ,    __UpperCamelCase=9_9       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=2       ,    __UpperCamelCase=4       ,    __UpperCamelCase=4       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=2_0       ,    __UpperCamelCase=2       ,    __UpperCamelCase=1       ,    __UpperCamelCase=0       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=1_6       ,    )->						Union[str, Any]:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       seq_length
           __lowerCAmelCase             =       is_training
           __lowerCAmelCase             =       use_labels
           __lowerCAmelCase             =       vocab_size
           __lowerCAmelCase             =       hidden_size
           __lowerCAmelCase             =       num_hidden_layers
           __lowerCAmelCase             =       num_attention_heads
           __lowerCAmelCase             =       intermediate_size
           __lowerCAmelCase             =       hidden_act
           __lowerCAmelCase             =       hidden_dropout_prob
           __lowerCAmelCase             =       attention_probs_dropout_prob
           __lowerCAmelCase             =       max_position_embeddings
           __lowerCAmelCase             =       eos_token_id
           __lowerCAmelCase             =       pad_token_id
           __lowerCAmelCase             =       bos_token_id
           __lowerCAmelCase             =       embed_dim
           __lowerCAmelCase             =       word_embed_proj_dim
           __lowerCAmelCase             =       False


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       ids_tensor([self.batch_size, self.seq_length - 1]       ,    self.vocab_size						)
           __lowerCAmelCase             =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)       ,    1						)
           __lowerCAmelCase             =       tf.concat([input_ids, eos_tensor]       ,    axis=1						)

           __lowerCAmelCase             =       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=__UpperCamelCase       ,    **self.config_updates       ,    )
           __lowerCAmelCase             =       prepare_opt_inputs_dict(__UpperCamelCase       ,    __UpperCamelCase						)
           return config, inputs_dict


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       TFOPTModel(config=__UpperCamelCase						)
           __lowerCAmelCase             =       inputs_dict["input_ids"]

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

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

           __lowerCAmelCase      ,			__lowerCAmelCase             =       outputs.to_tuple()

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

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

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

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

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

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



@require_tf
class 				_UpperCamelCase      (a_				,		a_				,		unittest.TestCase      ):
      snake_case_	=				(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
      snake_case_	=				(TFOPTForCausalLM,) if is_tf_available() else ()
      snake_case_	=				(
          {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
      )
      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				10


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       TFOPTModelTester(self						)
           __lowerCAmelCase             =       ConfigTester(self       ,    config_class=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           self.config_tester.run_common_tests()


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           def _get_word_embedding_weight(__UpperCamelCase       ,    __UpperCamelCase						):
                if hasattr(__UpperCamelCase       ,    "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(__UpperCamelCase       ,    "weight"						):
                          return embedding_layer.weight
                     else:
                          return None

           for model_class in self.all_model_classes:
                for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
                     # build the embeddings
                     __lowerCAmelCase             =       model_class(config=__UpperCamelCase						)
                     __lowerCAmelCase             =       _get_word_embedding_weight(__UpperCamelCase       ,    model.get_input_embeddings()						)
                     __lowerCAmelCase             =       _get_word_embedding_weight(__UpperCamelCase       ,    model.get_output_embeddings()						)

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

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

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

                     # check that weights remain the same after resizing
                     __lowerCAmelCase             =       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:
                               __lowerCAmelCase             =       False
                     self.assertTrue(__UpperCamelCase						)

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

                          __lowerCAmelCase             =       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:
                                    __lowerCAmelCase             =       False
                          self.assertTrue(__UpperCamelCase						)





def    __lowerCAmelCase   (    __snake_case							):
     return tf.constant(__snake_case       ,			dtype=tf.intaa							)



@require_tf
class 				_UpperCamelCase      (unittest.TestCase      ):
      snake_case_	=				99


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       tf.ones((4, 1)       ,    dtype=tf.intaa						) * 2
           __lowerCAmelCase             =       tf.concat([ids_tensor((4, 6)       ,    self.vocab_size - 3						) + 3, eos_column_vector]       ,    axis=1						)
           __lowerCAmelCase             =       input_ids.shape[0]
           __lowerCAmelCase             =       OPTConfig(
               vocab_size=self.vocab_size       ,    hidden_size=2_4       ,    num_hidden_layers=2       ,    num_attention_heads=2       ,    ffn_dim=3_2       ,    max_position_embeddings=4_8       ,    eos_token_id=2       ,    pad_token_id=1       ,    bos_token_id=0       ,    )
           return config, input_ids, batch_size



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


      @slow
      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       TFOPTModel.from_pretrained("facebook/opt-350m"						)
           __lowerCAmelCase             =       _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
           __lowerCAmelCase             =       tf.not_equal(__UpperCamelCase       ,    model.config.pad_token_id						)
           with tf.GradientTape():
                __lowerCAmelCase             =       model(input_ids=__UpperCamelCase       ,    attention_mask=__UpperCamelCase						).last_hidden_state
           __lowerCAmelCase             =       (1, 1_1, 5_1_2)
           self.assertEqual(output.shape       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       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]       ,    __UpperCamelCase       ,    atol=4e-3						)						)

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



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


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


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       TFOPTForCausalLM.from_pretrained(self.path_model						)
           __lowerCAmelCase             =       GPTaTokenizer.from_pretrained(self.path_model						)

           __lowerCAmelCase             =       [
               "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
           __lowerCAmelCase             =       tokenizer(__UpperCamelCase       ,    return_tensors="tf"       ,    padding=__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase						)
           __lowerCAmelCase             =       tf.math.reduce_mean(model(inputs.input_ids       ,    attention_mask=inputs.attention_mask						)[0]       ,    axis=-1						)
           __lowerCAmelCase             =       tf.constant(
               [
                   [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                   [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -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, -1_4.1_6_5_0, 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, -1_0.7_9_2_6, -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(__UpperCamelCase       ,    __UpperCamelCase       ,    atol=1e-4						)						)

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



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


      @property
      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           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 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "facebook/opt-125m"

           __lowerCAmelCase             =       [
               "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",
           ]

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

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

                __lowerCAmelCase             =       model.generate(__UpperCamelCase       ,    max_length=1_0						)

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

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       "facebook/opt-350m"

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

           __lowerCAmelCase             =       "left"

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

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

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

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

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

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

           __lowerCAmelCase             =       [
               "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]						)


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

           __lowerCAmelCase             =       [
               "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",
           ]

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

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

                __lowerCAmelCase             =       model.generate(__UpperCamelCase       ,    max_length=1_0						)

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

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)














from __future__ import annotations

import os
from typing import Any

import requests

lowerCamelCase      :       Tuple    =						'''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowerCamelCase      :       int    =						BASE_URL + '''/user'''

# https://github.com/settings/tokens
lowerCamelCase      :       Any    =						os.environ.get('''USER_TOKEN''', '''''')





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       {
         "Authorization": F"""token {auth_token}""",
         "Accept": "application/vnd.github.v3+json",
     }
     return requests.get(__snake_case       ,			headers=__snake_case							).json()


if __name__ == "__main__":  # pragma: no cover
 if USER_TOKEN:
  for key, value in fetch_github_info(USER_TOKEN).items():
   print(F'''{key}: {value}''')
 else:
  raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')






