





'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, 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
 SCREAMING_SNAKE_CASE__      					=     """platform"""

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						Dict     ,_snake_case  :						str=None     ,_snake_case  :						int=None     ,_snake_case  :						Optional[Any]=None     ,_snake_case  :						List[str]=None     ,_snake_case  :						Union[str, Any]=None     ,_snake_case  :						Any=None     ,):





    '''simple docstring'''


    if attention_mask is None:
        lowercase__		       =						np.where(input_ids != config.pad_token_id     ,1     ,0       )
    if decoder_attention_mask is None:
        lowercase__		       =						np.where(decoder_input_ids != config.pad_token_id     ,1     ,0       )
    if head_mask is None:
        lowercase__		       =						np.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
        lowercase__		       =						np.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
        lowercase__		       =						np.ones((config.decoder_layers, config.decoder_attention_heads)       )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }


class 				snake_case :



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=13     ,UpperCAmelCase_=7     ,UpperCAmelCase_=True     ,UpperCAmelCase_=False     ,UpperCAmelCase_=99     ,UpperCAmelCase_=16     ,UpperCAmelCase_=2     ,UpperCAmelCase_=4     ,UpperCAmelCase_=4     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=32     ,UpperCAmelCase_=2     ,UpperCAmelCase_=1     ,UpperCAmelCase_=0     ,UpperCAmelCase_=0.02     ,)  ->							int:
           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__		       =						initializer_range



       def 							_a      (				self      )  ->							Tuple:
           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__		       =						shift_tokens_right(snake_case_     ,1     ,2      )

           lowercase__		       =						BlenderbotSmallConfig(
               vocab_size=self.vocab_size     ,d_model=self.hidden_size     ,encoder_layers=self.num_hidden_layers     ,decoder_layers=self.num_hidden_layers     ,encoder_attention_heads=self.num_attention_heads     ,decoder_attention_heads=self.num_attention_heads     ,encoder_ffn_dim=self.intermediate_size     ,decoder_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     ,initializer_range=self.initializer_range     ,use_cache=snake_case_     ,)
           lowercase__		       =						prepare_blenderbot_inputs_dict(snake_case_     ,snake_case_     ,snake_case_      )
           return config, inputs_dict



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.prepare_config_and_inputs()
           return config, inputs_dict



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						20
           lowercase__		       =						model_class_name(snake_case_      )

           lowercase__		       =						model.encode(inputs_dict["input_ids"]      )

           lowercase__		       =						(
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           lowercase__		       =						model.init_cache(decoder_input_ids.shape[0]     ,snake_case_     ,snake_case_      )
           lowercase__		       =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,dtype="i4"      )

           lowercase__		       =						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)
           lowercase__		       =						model.decode(
               decoder_input_ids[:, :-1]     ,snake_case_     ,decoder_attention_mask=snake_case_     ,past_key_values=snake_case_     ,decoder_position_ids=snake_case_     ,)

           lowercase__		       =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype="i4"      )
           lowercase__		       =						model.decode(
               decoder_input_ids[:, -1:]     ,snake_case_     ,decoder_attention_mask=snake_case_     ,past_key_values=outputs_cache.past_key_values     ,decoder_position_ids=snake_case_     ,)

           lowercase__		       =						model.decode(snake_case_     ,snake_case_      )

           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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						20
           lowercase__		       =						model_class_name(snake_case_      )

           lowercase__		       =						model.encode(inputs_dict["input_ids"]      )

           lowercase__		       =						(
               inputs_dict["decoder_input_ids"],
               inputs_dict["decoder_attention_mask"],
           )

           lowercase__		       =						jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
               ]     ,axis=-1     ,)

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

           lowercase__		       =						model.decode(
               decoder_input_ids[:, :-1]     ,snake_case_     ,decoder_attention_mask=snake_case_     ,past_key_values=snake_case_     ,decoder_position_ids=snake_case_     ,)
           lowercase__		       =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype="i4"      )
           lowercase__		       =						model.decode(
               decoder_input_ids[:, -1:]     ,snake_case_     ,past_key_values=outputs_cache.past_key_values     ,decoder_attention_mask=snake_case_     ,decoder_position_ids=snake_case_     ,)

           lowercase__		       =						model.decode(snake_case_     ,snake_case_     ,decoder_attention_mask=snake_case_      )

           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 				snake_case (unittest.TestCase   ):
       lowerCAmelCase__  :List[str]						   =					99



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						np.array(
               [
                   [71, 82, 18, 33, 46, 91, 2],
                   [68, 34, 26, 58, 30, 82, 2],
                   [5, 97, 17, 39, 94, 40, 2],
                   [76, 83, 94, 25, 70, 78, 2],
                   [87, 59, 41, 35, 48, 66, 2],
                   [55, 13, 16, 58, 5, 2, 1],  # note padding
                   [64, 27, 31, 51, 12, 75, 2],
                   [52, 64, 86, 17, 83, 39, 2],
                   [48, 61, 9, 24, 71, 82, 2],
                   [26, 1, 60, 48, 22, 13, 2],
                   [21, 5, 62, 28, 14, 76, 2],
                   [45, 98, 37, 86, 59, 48, 2],
                   [70, 70, 50, 9, 28, 0, 2],
               ]     ,dtype=np.intaa     ,)

           lowercase__		       =						input_ids.shape[0]
           lowercase__		       =						BlenderbotSmallConfig(
               vocab_size=self.vocab_size     ,d_model=24     ,encoder_layers=2     ,decoder_layers=2     ,encoder_attention_heads=2     ,decoder_attention_heads=2     ,encoder_ffn_dim=32     ,decoder_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



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						self._get_config_and_data()
           lowercase__		       =						FlaxBlenderbotSmallForConditionalGeneration(snake_case_      )
           lowercase__		       =						lm_model(input_ids=snake_case_      )
           lowercase__		       =						(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["logits"].shape     ,snake_case_      )



       def 							_a      (				self      )  ->							int:
           lowercase__		       =						BlenderbotSmallConfig(
               vocab_size=self.vocab_size     ,d_model=14     ,encoder_layers=2     ,decoder_layers=2     ,encoder_attention_heads=2     ,decoder_attention_heads=2     ,encoder_ffn_dim=8     ,decoder_ffn_dim=8     ,max_position_embeddings=48     ,)
           lowercase__		       =						FlaxBlenderbotSmallForConditionalGeneration(snake_case_      )
           lowercase__		       =						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,dtype=np.intaa      )
           lowercase__		       =						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,dtype=np.intaa      )
           lowercase__		       =						lm_model(input_ids=snake_case_     ,decoder_input_ids=snake_case_      )
           lowercase__		       =						(*summary.shape, config.vocab_size)
           self.assertEqual(outputs["logits"].shape     ,snake_case_      )







       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,dtype=np.intaa      )
           lowercase__		       =						shift_tokens_right(snake_case_     ,1     ,2      )
           lowercase__		       =						np.equal(snake_case_     ,1      ).astype(np.floataa      ).sum()
           lowercase__		       =						np.equal(snake_case_     ,1      ).astype(np.floataa      ).sum()
           self.assertEqual(shifted.shape     ,input_ids.shape      )
           self.assertEqual(snake_case_     ,n_pad_before - 1      )
           self.assertTrue(np.equal(shifted[:, 0]     ,2      ).all()      )





@require_flax
class 				snake_case (_snake_case		,      unittest.TestCase		,      _snake_case   ):
       lowerCAmelCase__  :str						   =					True
       lowerCAmelCase__  :Optional[Any]						   =					(
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       lowerCAmelCase__  :Dict						   =					(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						FlaxBlenderbotSmallModelTester(self      )



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



       def 							_a      (				self      )  ->							Dict:
           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(snake_case_     ,snake_case_     ,snake_case_      )



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                   lowercase__		       =						self._prepare_for_class(snake_case_     ,snake_case_      )
                   lowercase__		       =						model_class(snake_case_      )

                   @jax.jit
                   def encode_jitted(UpperCAmelCase_     ,UpperCAmelCase_=None     ,**UpperCAmelCase_      ):
                       return model.encode(input_ids=snake_case_     ,attention_mask=snake_case_      )

                   with self.subTest("JIT Enabled"      ):
                       lowercase__		       =						encode_jitted(**snake_case_      ).to_tuple()

                   with self.subTest("JIT Disabled"      ):
                       with jax.disable_jit():
                           lowercase__		       =						encode_jitted(**snake_case_      ).to_tuple()

                   self.assertEqual(len(snake_case_      )     ,len(snake_case_      )      )
                   for jitted_output, output in zip(snake_case_     ,snake_case_      ):
                       self.assertEqual(jitted_output.shape     ,output.shape      )



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                   lowercase__		       =						model_class(snake_case_      )
                   lowercase__		       =						model.encode(inputs_dict["input_ids"]     ,inputs_dict["attention_mask"]      )

                   lowercase__		       =						{
                       "decoder_input_ids": inputs_dict["decoder_input_ids"],
                       "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                       "encoder_outputs": encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      ):
                       return model.decode(
                           decoder_input_ids=snake_case_     ,decoder_attention_mask=snake_case_     ,encoder_outputs=snake_case_     ,)

                   with self.subTest("JIT Enabled"      ):
                       lowercase__		       =						decode_jitted(**snake_case_      ).to_tuple()

                   with self.subTest("JIT Disabled"      ):
                       with jax.disable_jit():
                           lowercase__		       =						decode_jitted(**snake_case_      ).to_tuple()

                   self.assertEqual(len(snake_case_      )     ,len(snake_case_      )      )
                   for jitted_output, output in zip(snake_case_     ,snake_case_      ):
                       self.assertEqual(jitted_output.shape     ,output.shape      )







       @slow
       def 							_a      (				self      )  ->							str:
           for model_class_name in self.all_model_classes:
               lowercase__		       =						model_class_name.from_pretrained("facebook/blenderbot_small-90M"      )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               lowercase__		       =						np.ones((1, 1)      ) * model.config.eos_token_id
               lowercase__		       =						model(snake_case_      )
               self.assertIsNotNone(snake_case_      )









'''simple docstring'''





import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


SCREAMING_SNAKE_CASE__      					=     "http://www.mocksite.com/file1.txt"
SCREAMING_SNAKE_CASE__      					=     "\"text\": [\"foo\", \"foo\"]"
SCREAMING_SNAKE_CASE__      					=     "6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"


class 				snake_case :
       lowerCAmelCase__  :Union[str, Any]						   =					200
       lowerCAmelCase__  :Tuple						   =					{"Content-Length": "100"}
       lowerCAmelCase__  :Any						   =					{}



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							List[Any]:
           return [bytes(UpperCAmelCase_     ,"utf-8"      )]





def        lowerCamelCase  (				*_snake_case  :						int     ,**_snake_case  :						int       ):





    '''simple docstring'''


    return MockResponse()




@pytest.mark.parametrize("urls_type"     ,[str, list, dict]       )
def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						List[Any]     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    import requests

    monkeypatch.setattr(_snake_case     ,"request"     ,_snake_case       )

    lowercase__		       =						URL
    if issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						url
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						[url]
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						{"train": url}
    lowercase__		       =						"dummy"
    lowercase__		       =						"downloads"
    lowercase__		       =						tmp_path
    lowercase__		       =						DownloadConfig(
        cache_dir=os.path.join(_snake_case     ,_snake_case       )     ,use_etag=_snake_case     ,)
    lowercase__		       =						DownloadManager(dataset_name=_snake_case     ,download_config=_snake_case       )
    lowercase__		       =						dl_manager.download(_snake_case       )
    lowercase__		       =						urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_snake_case     ,_snake_case       ):
            lowercase__		       =						[downloaded_paths]
            lowercase__		       =						[urls]
        elif isinstance(_snake_case     ,_snake_case       ):
            assert "train" in downloaded_paths.keys()
            lowercase__		       =						downloaded_paths.values()
            lowercase__		       =						urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_snake_case     ,_snake_case       ):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            lowercase__		       =						Path(_snake_case       )
            lowercase__		       =						downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            lowercase__		       =						downloaded_path.read_text()
            assert content == CONTENT
            lowercase__		       =						downloaded_path.with_suffix(".json"       )
            assert metadata_downloaded_path.exists()
            lowercase__		       =						json.loads(metadata_downloaded_path.read_text()       )
            assert metadata_content == {"url": URL, "etag": None}




@pytest.mark.parametrize("paths_type"     ,[str, list, dict]       )
def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						List[str]     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						str(_snake_case       )
    if issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						filename
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						[filename]
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						{"train": filename}
    lowercase__		       =						"dummy"
    lowercase__		       =						xz_file.parent
    lowercase__		       =						"extracted"
    lowercase__		       =						DownloadConfig(
        cache_dir=_snake_case     ,use_etag=_snake_case     ,)
    lowercase__		       =						DownloadManager(dataset_name=_snake_case     ,download_config=_snake_case       )
    lowercase__		       =						dl_manager.extract(_snake_case       )
    lowercase__		       =						paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_snake_case     ,_snake_case       ):
            lowercase__		       =						[extracted_paths]
            lowercase__		       =						[paths]
        elif isinstance(_snake_case     ,_snake_case       ):
            assert "train" in extracted_paths.keys()
            lowercase__		       =						extracted_paths.values()
            lowercase__		       =						paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_snake_case     ,_snake_case       ):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            lowercase__		       =						Path(_snake_case       )
            lowercase__		       =						extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_snake_case     ,etag=_snake_case       )
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            lowercase__		       =						extracted_path.read_text()
            lowercase__		       =						text_file.read_text()
            assert extracted_file_content == expected_file_content




def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    assert path.endswith(".jsonl"       )
    for num_items, line in enumerate(_snake_case     ,start=1       ):
        lowercase__		       =						json.loads(line.decode("utf-8"       )       )
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4




@pytest.mark.parametrize("archive_jsonl"     ,["tar_jsonl_path", "zip_jsonl_path"]       )
def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						request.getfixturevalue(_snake_case       )
    lowercase__		       =						DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
        _test_jsonl(_snake_case     ,_snake_case       )
    assert num_jsonl == 2




@pytest.mark.parametrize("archive_nested_jsonl"     ,["tar_nested_jsonl_path", "zip_nested_jsonl_path"]       )
def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						Dict       ):





    '''simple docstring'''


    lowercase__		       =						request.getfixturevalue(_snake_case       )
    lowercase__		       =						DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
            _test_jsonl(_snake_case     ,_snake_case       )
    assert num_tar == 1
    assert num_jsonl == 2




def        lowerCamelCase  (				_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_snake_case       )     ,start=1       ):
        assert os.path.basename(_snake_case       ) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2




